

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__  ( lowercase_					,  lowercase_     ):







			@register_to_config
			def __init__(			self			: Tuple			,  lowercase			: List[str] = 1_28			,  lowercase			: List[str] = 2_56			,  lowercase			: List[str] = 20_00.0			,  lowercase			: Tuple = 7_68			,  lowercase			: Union[str, Any] = 12			,  lowercase			: int = 12			,  lowercase			: str = 64			,  lowercase			: List[Any] = 20_48			,  lowercase			: Dict = 0.1			,  ):





								'''simple docstring'''



								super().__init__()

								UpperCAmelCase			:   Dict      =				nn.Sequential(
								    nn.Linear(lowercase			,  d_model * 4			,  bias=lowercase  )			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowercase  )			,  nn.SiLU()			,  )

								UpperCAmelCase			:   Optional[int]      =				nn.Embedding(lowercase			,  lowercase  )
								UpperCAmelCase			:   str      =				False

								UpperCAmelCase			:   str      =				nn.Linear(lowercase			,  lowercase			,  bias=lowercase  )

								UpperCAmelCase			:   Any      =				nn.Dropout(p=lowercase  )

								UpperCAmelCase			:   Optional[int]      =				nn.ModuleList()
								for lyr_num in range(lowercase  ):
													# FiLM conditional T5 decoder
													UpperCAmelCase			:   List[Any]      =				DecoderLayer(d_model=lowercase			,  d_kv=lowercase			,  num_heads=lowercase			,  d_ff=lowercase			,  dropout_rate=lowercase  )
													self.decoders.append(lowercase  )

								UpperCAmelCase			:   List[str]      =				TaLayerNorm(lowercase  )

								UpperCAmelCase			:   Tuple      =				nn.Dropout(p=lowercase  )
								UpperCAmelCase			:   Optional[int]      =				nn.Linear(lowercase			,  lowercase			,  bias=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Optional[Any]			,  lowercase			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				torch.mul(query_input.unsqueeze(-1  )			,  key_input.unsqueeze(-2  )  )
								return mask.unsqueeze(-3  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: Optional[int]			,  lowercase			: Optional[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   List[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			:   Optional[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			:   List[Any]      =				self.conditioning_emb(lowercase  ).unsqueeze(1  )

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

								UpperCAmelCase			:   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			:   Tuple      =				torch.broadcast_to(
								    torch.arange(lowercase			,  device=decoder_input_tokens.device  )			,  (batch, seq_length)			,  )

								UpperCAmelCase			:   Union[str, Any]      =				self.position_encoding(lowercase  )

								UpperCAmelCase			:   Optional[Any]      =				self.continuous_inputs_projection(lowercase  )
								inputs += position_encodings
								UpperCAmelCase			:   int      =				self.dropout(lowercase  )

								# 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			:   List[Any]      =				[(x, self.encoder_decoder_mask(lowercase			,  lowercase  )) for x, y in encodings_and_masks]

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

								for lyr in self.decoders:
													UpperCAmelCase			:   Union[str, Any]      =				lyr(
													    lowercase			,  conditioning_emb=lowercase			,  encoder_hidden_states=lowercase			,  encoder_attention_mask=lowercase			,  )[0]

								UpperCAmelCase			:   Tuple      =				self.decoder_norm(lowercase  )
								UpperCAmelCase			:   Dict      =				self.post_dropout(lowercase  )

								UpperCAmelCase			:   int      =				self.spec_out(lowercase  )
								return spec_out







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: int			,  lowercase			: Tuple			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]			,  lowercase			: Dict			,  lowercase			: Tuple			,  lowercase			: Optional[int]=1E-6  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   str      =				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 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Dict			,  lowercase			: Union[str, Any]=None			,  lowercase			: Optional[Any]=None			,  lowercase			: str=None			,  lowercase			: Tuple=None			,  lowercase			: List[Any]=None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				self.layer[0](
								    lowercase			,  conditioning_emb=lowercase			,  attention_mask=lowercase			,  )

								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](
													    lowercase			,  key_value_states=lowercase			,  attention_mask=lowercase			,  )

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

								return (hidden_states,)







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: Union[str, Any]			,  lowercase			: Optional[int]			,  lowercase			: Tuple			,  lowercase			: Dict			,  lowercase			: int  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Any      =				TaLayerNorm(lowercase  )
								UpperCAmelCase			:   Any      =				TaFiLMLayer(in_features=d_model * 4			,  out_features=lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				Attention(query_dim=lowercase			,  heads=lowercase			,  dim_head=lowercase			,  out_bias=lowercase			,  scale_qk=lowercase  )
								UpperCAmelCase			:   Any      =				nn.Dropout(lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: Dict			,  lowercase			: Any=None			,  lowercase			: List[Any]=None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.layer_norm(lowercase  )

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

								# Self-attention block
								UpperCAmelCase			:   Dict      =				self.attention(lowercase  )

								UpperCAmelCase			:   Tuple      =				hidden_states + self.dropout(lowercase  )

								return hidden_states







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: str			,  lowercase			: Dict			,  lowercase			: int			,  lowercase			: Any			,  lowercase			: str			,  lowercase			: Any  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Dict      =				Attention(query_dim=lowercase			,  heads=lowercase			,  dim_head=lowercase			,  out_bias=lowercase			,  scale_qk=lowercase  )
								UpperCAmelCase			:   Any      =				TaLayerNorm(lowercase			,  eps=lowercase  )
								UpperCAmelCase			:   Dict      =				nn.Dropout(lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Any			,  lowercase			: Any=None			,  lowercase			: int=None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.layer_norm(lowercase  )
								UpperCAmelCase			:   List[str]      =				self.attention(
								    lowercase			,  encoder_hidden_states=lowercase			,  attention_mask=attention_mask.squeeze(1  )			,  )
								UpperCAmelCase			:   Any      =				hidden_states + self.dropout(lowercase  )
								return layer_output







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: Tuple			,  lowercase			: Union[str, Any]			,  lowercase			: Any			,  lowercase			: List[str]			,  lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Dict      =				TaDenseGatedActDense(d_model=lowercase			,  d_ff=lowercase			,  dropout_rate=lowercase  )
								UpperCAmelCase			:   Dict      =				TaFiLMLayer(in_features=d_model * 4			,  out_features=lowercase  )
								UpperCAmelCase			:   int      =				TaLayerNorm(lowercase			,  eps=lowercase  )
								UpperCAmelCase			:   Optional[int]      =				nn.Dropout(lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: Dict			,  lowercase			: Optional[Any]=None  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				self.layer_norm(lowercase  )
								if conditioning_emb is not None:
													UpperCAmelCase			:   str      =				self.film(lowercase			,  lowercase  )

								UpperCAmelCase			:   Any      =				self.DenseReluDense(lowercase  )
								UpperCAmelCase			:   List[str]      =				hidden_states + self.dropout(lowercase  )
								return hidden_states







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: Any			,  lowercase			: List[str]			,  lowercase			: Union[str, Any]			,  lowercase			: int  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   List[str]      =				nn.Linear(lowercase			,  lowercase			,  bias=lowercase  )
								UpperCAmelCase			:   int      =				nn.Linear(lowercase			,  lowercase			,  bias=lowercase  )
								UpperCAmelCase			:   List[Any]      =				nn.Linear(lowercase			,  lowercase			,  bias=lowercase  )
								UpperCAmelCase			:   int      =				nn.Dropout(lowercase  )
								UpperCAmelCase			:   List[str]      =				NewGELUActivation()







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				self.act(self.wi_a(lowercase  )  )
								UpperCAmelCase			:   int      =				self.wi_a(lowercase  )
								UpperCAmelCase			:   List[str]      =				hidden_gelu * hidden_linear
								UpperCAmelCase			:   Dict      =				self.dropout(lowercase  )

								UpperCAmelCase			:   Dict      =				self.wo(lowercase  )
								return hidden_states







class       snake_case__  ( nn.Module     ):







			def __init__(			self			: int			,  lowercase			: Tuple			,  lowercase			: str=1E-6  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Dict      =				nn.Parameter(torch.ones(lowercase  )  )
								UpperCAmelCase			:   int      =				eps







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				hidden_states.to(torch.floataa  ).pow(2  ).mean(-1			,  keepdim=lowercase  )
								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			:   Tuple      =				hidden_states.to(self.weight.dtype  )

								return self.weight * hidden_states







class       snake_case__  ( nn.Module     ):







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								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       snake_case__  ( nn.Module     ):







			def __init__(			self			: Tuple			,  lowercase			: List[Any]			,  lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Optional[Any]      =				nn.Linear(lowercase			,  out_features * 2			,  bias=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: List[str]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				self.scale_bias(lowercase  )
								UpperCAmelCase      ,							UpperCAmelCase			:   List[Any]      =				torch.chunk(lowercase			,  2			,  -1  )
								UpperCAmelCase			:   List[Any]      =				x * (1 + scale) + shift
								return x





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	snake_case_   :   List[str]        =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


