


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__(							UpperCAmelCase__,						UpperCAmelCase__							):







						'''simple docstring'''




						@register_to_config
						def __init__(				self   ,						__lowercase = 1_2_8   ,						__lowercase = 2_5_6   ,						__lowercase = 20_00.0   ,						__lowercase = 7_6_8   ,						__lowercase = 1_2   ,						__lowercase = 1_2   ,						__lowercase = 6_4   ,						__lowercase = 2_0_4_8   ,						__lowercase = 0.1   ,						)					->    List[str]:
										super().__init__()

										lowerCAmelCase_ :      Optional[int]    				=				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_ :      Tuple    				=				nn.Embedding(__lowercase   ,						__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				False

										lowerCAmelCase_ :      List[Any]    				=				nn.Linear(__lowercase   ,						__lowercase   ,						bias=__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				nn.Dropout(p=__lowercase							)

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

										lowerCAmelCase_ :      str    				=				TaLayerNorm(__lowercase							)

										lowerCAmelCase_ :      str    				=				nn.Dropout(p=__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				nn.Linear(__lowercase   ,						__lowercase   ,						bias=__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      int    				=				torch.mul(query_input.unsqueeze(-1							)   ,						key_input.unsqueeze(-2							)							)
										return mask.unsqueeze(-3							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      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.
										lowerCAmelCase_ :      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							)

										lowerCAmelCase_ :      Optional[int]    				=				self.conditioning_emb(__lowercase							).unsqueeze(1							)

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

										lowerCAmelCase_ :      int    				=				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_ :      Union[str, Any]    				=				torch.broadcast_to(
										    torch.arange(__lowercase   ,						device=decoder_input_tokens.device							)   ,						(batch, seq_length)   ,						)

										lowerCAmelCase_ :      List[str]    				=				self.position_encoding(__lowercase							)

										lowerCAmelCase_ :      Dict    				=				self.continuous_inputs_projection(__lowercase							)
										inputs += position_encodings
										lowerCAmelCase_ :      List[str]    				=				self.dropout(__lowercase							)

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

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

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

										for lyr in self.decoders:
														lowerCAmelCase_ :      Any    				=				lyr(
														    __lowercase   ,						conditioning_emb=__lowercase   ,						encoder_hidden_states=__lowercase   ,						encoder_attention_mask=__lowercase   ,						)[0]

										lowerCAmelCase_ :      int    				=				self.decoder_norm(__lowercase							)
										lowerCAmelCase_ :      Tuple    				=				self.post_dropout(__lowercase							)

										lowerCAmelCase_ :      Any    				=				self.spec_out(__lowercase							)
										return spec_out







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase=1e-6							)					->    Union[str, Any]:
										super().__init__()
										lowerCAmelCase_ :      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 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=None   ,						)					->    Dict:
										lowerCAmelCase_ :      Any    				=				self.layer[0](
										    __lowercase   ,						conditioning_emb=__lowercase   ,						attention_mask=__lowercase   ,						)

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

														lowerCAmelCase_ :      List[Any]    				=				self.layer[1](
														    __lowercase   ,						key_value_states=__lowercase   ,						attention_mask=__lowercase   ,						)

										# Apply Film Conditional Feed Forward layer
										lowerCAmelCase_ :      Optional[Any]    				=				self.layer[-1](__lowercase   ,						__lowercase							)

										return (hidden_states,)







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None   ,						__lowercase=None   ,						)					->    Union[str, Any]:
										# pre_self_attention_layer_norm
										lowerCAmelCase_ :      int    				=				self.layer_norm(__lowercase							)

										if conditioning_emb is not None:
														lowerCAmelCase_ :      Dict    				=				self.FiLMLayer(__lowercase   ,						__lowercase							)

										# Self-attention block
										lowerCAmelCase_ :      List[Any]    				=				self.attention(__lowercase							)

										lowerCAmelCase_ :      str    				=				hidden_states + self.dropout(__lowercase							)

										return hidden_states







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Dict:
										super().__init__()
										lowerCAmelCase_ :      Any    				=				Attention(query_dim=__lowercase   ,						heads=__lowercase   ,						dim_head=__lowercase   ,						out_bias=__lowercase   ,						scale_qk=__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				TaLayerNorm(__lowercase   ,						eps=__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				nn.Dropout(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None   ,						__lowercase=None   ,						)					->    Any:
										lowerCAmelCase_ :      Any    				=				self.layer_norm(__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				self.attention(
										    __lowercase   ,						encoder_hidden_states=__lowercase   ,						attention_mask=attention_mask.squeeze(1							)   ,						)
										lowerCAmelCase_ :      Tuple    				=				hidden_states + self.dropout(__lowercase							)
										return layer_output







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										super().__init__()
										lowerCAmelCase_ :      Dict    				=				TaDenseGatedActDense(d_model=__lowercase   ,						d_ff=__lowercase   ,						dropout_rate=__lowercase							)
										lowerCAmelCase_ :      Any    				=				TaFiLMLayer(in_features=d_model * 4   ,						out_features=__lowercase							)
										lowerCAmelCase_ :      Dict    				=				TaLayerNorm(__lowercase   ,						eps=__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				nn.Dropout(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None							)					->    Optional[int]:
										lowerCAmelCase_ :      Any    				=				self.layer_norm(__lowercase							)
										if conditioning_emb is not None:
														lowerCAmelCase_ :      Tuple    				=				self.film(__lowercase   ,						__lowercase							)

										lowerCAmelCase_ :      int    				=				self.DenseReluDense(__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				hidden_states + self.dropout(__lowercase							)
										return hidden_states







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




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




						def 						lowercase_ (				self   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      List[Any]    				=				self.act(self.wi_a(__lowercase							)							)
										lowerCAmelCase_ :      List[Any]    				=				self.wi_a(__lowercase							)
										lowerCAmelCase_ :      int    				=				hidden_gelu * hidden_linear
										lowerCAmelCase_ :      Dict    				=				self.dropout(__lowercase							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.wo(__lowercase							)
										return hidden_states







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=1e-6							)					->    List[Any]:
										super().__init__()
										lowerCAmelCase_ :      Union[str, Any]    				=				nn.Parameter(torch.ones(__lowercase							)							)
										lowerCAmelCase_ :      Dict    				=				eps




						def 						lowercase_ (				self   ,						__lowercase							)					->    Dict:
										# 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_ :      str    				=				hidden_states.to(torch.floataa							).pow(2							).mean(-1   ,						keepdim=__lowercase							)
										lowerCAmelCase_ :      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]:
														lowerCAmelCase_ :      List[Any]    				=				hidden_states.to(self.weight.dtype							)

										return self.weight * hidden_states







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def 						lowercase_ (				self   ,						__lowercase							)					->    torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.04_47_15 * torch.pow(__lowercase   ,						3.0							))							))







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				self.scale_bias(__lowercase							)
										lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				torch.chunk(__lowercase   ,						2   ,						-1							)
										lowerCAmelCase_ :      Dict    				=				x * (1 + scale) + shift
										return x



import re



def 			lowerCAmelCase (		lowerCAmelCase_ )-> bool:
				lowerCAmelCase_ :      Tuple    				=				re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
				if match := re.search(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								return match.string == phone
				return False


if __name__ == "__main__":
						print(indian_phone_validator("""+918827897895"""))