
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        _UpperCamelCase				(					_A     ,       _A  ):






			'''simple docstring'''

			@register_to_config
			def __init__(       self :       List[str]						,	snake_case_ :       int = 128						,	snake_case_ :       int = 256						,	snake_case_ :       float = 2000.0						,	snake_case_ :       int = 768						,	snake_case_ :       int = 12						,	snake_case_ :       int = 12						,	snake_case_ :       int = 64						,	snake_case_ :       int = 2048						,	snake_case_ :       float = 0.1						,	):
							super().__init__()

							UpperCamelCase_:							Tuple	=						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_:							Any	=						nn.Embedding(snake_case_						,	snake_case_      )
							UpperCamelCase_:							Tuple	=						False

							UpperCamelCase_:							Optional[int]	=						nn.Linear(snake_case_						,	snake_case_						,	bias=snake_case_      )

							UpperCamelCase_:							Tuple	=						nn.Dropout(p=snake_case_      )

							UpperCamelCase_:							int	=						nn.ModuleList()
							for lyr_num in range(snake_case_      ):
											# FiLM conditional T5 decoder
											UpperCamelCase_:							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_:							Union[str, Any]	=						TaLayerNorm(snake_case_      )

							UpperCamelCase_:							Tuple	=						nn.Dropout(p=snake_case_      )
							UpperCamelCase_:							Any	=						nn.Linear(snake_case_						,	snake_case_						,	bias=snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							List[str]	=						torch.mul(query_input.unsqueeze(-1      )						,	key_input.unsqueeze(-2      )      )
							return mask.unsqueeze(-3      )




			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       int      ):
							UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							str	=						decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							UpperCamelCase_:							Any	=						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_:							str	=						self.conditioning_emb(snake_case_      ).unsqueeze(1      )

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

							UpperCamelCase_:							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.
							UpperCamelCase_:							Union[str, Any]	=						torch.broadcast_to(
							    torch.arange(snake_case_						,	device=decoder_input_tokens.device      )						,	(batch, seq_length)						,	)

							UpperCamelCase_:							List[str]	=						self.position_encoding(snake_case_      )

							UpperCamelCase_:							List[Any]	=						self.continuous_inputs_projection(snake_case_      )
							inputs += position_encodings
							UpperCamelCase_:							Union[str, Any]	=						self.dropout(snake_case_      )

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

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

							# cross attend style: concat encodings
							UpperCamelCase_:							List[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_:							Union[str, Any]	=						lyr(
											    snake_case_						,	conditioning_emb=snake_case_						,	encoder_hidden_states=snake_case_						,	encoder_attention_mask=snake_case_						,	)[0]

							UpperCamelCase_:							Any	=						self.decoder_norm(snake_case_      )
							UpperCamelCase_:							Union[str, Any]	=						self.post_dropout(snake_case_      )

							UpperCamelCase_:							List[Any]	=						self.spec_out(snake_case_      )
							return spec_out




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Any						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Dict						,	snake_case_ :       List[str]=1e-6      ):
							super().__init__()
							UpperCamelCase_:							Union[str, 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       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Any						,	snake_case_ :       int=None						,	snake_case_ :       Dict=None						,	snake_case_ :       Tuple=None						,	snake_case_ :       str=None						,	snake_case_ :       Union[str, Any]=None						,	):
							UpperCamelCase_:							str	=						self.layer[0](
							    snake_case_						,	conditioning_emb=snake_case_						,	attention_mask=snake_case_						,	)

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

											UpperCamelCase_:							Any	=						self.layer[1](
											    snake_case_						,	key_value_states=snake_case_						,	attention_mask=snake_case_						,	)

							# Apply Film Conditional Feed Forward layer
							UpperCamelCase_:							Tuple	=						self.layer[-1](snake_case_						,	snake_case_      )

							return (hidden_states,)




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			def __init__(       self :       List[Any]						,	snake_case_ :       int						,	snake_case_ :       Optional[int]						,	snake_case_ :       Tuple						,	snake_case_ :       Any      ):
							super().__init__()
							UpperCamelCase_:							Optional[Any]	=						TaLayerNorm(snake_case_      )
							UpperCamelCase_:							Dict	=						TaFiLMLayer(in_features=d_model * 4						,	out_features=snake_case_      )
							UpperCamelCase_:							Dict	=						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       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Any=None						,	snake_case_ :       List[Any]=None						,	):
							# pre_self_attention_layer_norm
							UpperCamelCase_:							Union[str, Any]	=						self.layer_norm(snake_case_      )

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

							# Self-attention block
							UpperCamelCase_:							Optional[int]	=						self.attention(snake_case_      )

							UpperCamelCase_:							List[str]	=						hidden_states + self.dropout(snake_case_      )

							return hidden_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

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




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Any=None						,	):
							UpperCamelCase_:							Optional[Any]	=						self.layer_norm(snake_case_      )
							UpperCamelCase_:							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        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

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




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

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




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

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




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

							UpperCamelCase_:							Union[str, Any]	=						self.wo(snake_case_      )
							return hidden_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

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




			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       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

							UpperCamelCase_:							Tuple	=						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_:							Dict	=						hidden_states.to(self.weight.dtype      )

							return self.weight * hidden_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

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




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			def __init__(       self :       Optional[int]						,	snake_case_ :       Dict						,	snake_case_ :       int      ):
							super().__init__()
							UpperCamelCase_:							int	=						nn.Linear(snake_case_						,	out_features * 2						,	bias=snake_case_      )




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








import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_						: Dict         =  logging.get_logger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Tuple=None						,	**snake_case_ :       List[str]      ):
							warnings.warn(
							    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
							    """instead."""						,	snake_case_						,	)
							super().__init__(args=snake_case_						,	**snake_case_      )






