
"""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 							__magic_name__     (		lowercase__      ,		lowercase__   ):





					@register_to_config
					def __init__(				self   :							Optional[int]							,						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__()

												__snake_case     			=       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()							,						)

												__snake_case     			=       nn.Embedding(snake_case_							,						snake_case_    )
												__snake_case     			=       False

												__snake_case     			=       nn.Linear(snake_case_							,						snake_case_							,						bias=snake_case_    )

												__snake_case     			=       nn.Dropout(p=snake_case_    )

												__snake_case     			=       nn.ModuleList()
												for lyr_num in range(snake_case_    ):
																			# FiLM conditional T5 decoder
																			__snake_case     			=       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_    )

												__snake_case     			=       TaLayerNorm(snake_case_    )

												__snake_case     			=       nn.Dropout(p=snake_case_    )
												__snake_case     			=       nn.Linear(snake_case_							,						snake_case_							,						bias=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Any							,						snake_case_   :							str    ):
												__snake_case     			=       torch.mul(query_input.unsqueeze(-1    )							,						key_input.unsqueeze(-2    )    )
												return mask.unsqueeze(-3    )





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							int    ):
												__snake_case			,   __snake_case			,   __snake_case     			=       decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

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

												__snake_case     			=       self.conditioning_emb(snake_case_    ).unsqueeze(1    )

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

												__snake_case     			=       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.
												__snake_case     			=       torch.broadcast_to(
												    torch.arange(snake_case_							,						device=decoder_input_tokens.device    )							,						(batch, seq_length)							,						)

												__snake_case     			=       self.position_encoding(snake_case_    )

												__snake_case     			=       self.continuous_inputs_projection(snake_case_    )
												inputs += position_encodings
												__snake_case     			=       self.dropout(snake_case_    )

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

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

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

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

												__snake_case     			=       self.decoder_norm(snake_case_    )
												__snake_case     			=       self.post_dropout(snake_case_    )

												__snake_case     			=       self.spec_out(snake_case_    )
												return spec_out







class 							__magic_name__     (		nn.Module   ):





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

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

																			__snake_case     			=       self.layer[1](
																			    snake_case_							,						key_value_states=snake_case_							,						attention_mask=snake_case_							,						)

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

												return (hidden_states,)







class 							__magic_name__     (		nn.Module   ):





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





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]=None							,						snake_case_   :							List[Any]=None							,						):
												# pre_self_attention_layer_norm
												__snake_case     			=       self.layer_norm(snake_case_    )

												if conditioning_emb is not None:
																			__snake_case     			=       self.FiLMLayer(snake_case_							,						snake_case_    )

												# Self-attention block
												__snake_case     			=       self.attention(snake_case_    )

												__snake_case     			=       hidden_states + self.dropout(snake_case_    )

												return hidden_states







class 							__magic_name__     (		nn.Module   ):





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





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







class 							__magic_name__     (		nn.Module   ):





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





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							int							,						snake_case_   :							Optional[int]=None    ):
												__snake_case     			=       self.layer_norm(snake_case_    )
												if conditioning_emb is not None:
																			__snake_case     			=       self.film(snake_case_							,						snake_case_    )

												__snake_case     			=       self.DenseReluDense(snake_case_    )
												__snake_case     			=       hidden_states + self.dropout(snake_case_    )
												return hidden_states







class 							__magic_name__     (		nn.Module   ):





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





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							List[str]    ):
												__snake_case     			=       self.act(self.wi_a(snake_case_    )    )
												__snake_case     			=       self.wi_a(snake_case_    )
												__snake_case     			=       hidden_gelu * hidden_linear
												__snake_case     			=       self.dropout(snake_case_    )

												__snake_case     			=       self.wo(snake_case_    )
												return hidden_states







class 							__magic_name__     (		nn.Module   ):





					def __init__(				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							Tuple=1e-6    ):
												super().__init__()
												__snake_case     			=       nn.Parameter(torch.ones(snake_case_    )    )
												__snake_case     			=       eps





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[str]    ):
												# 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

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

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

												return self.weight * hidden_states







class 							__magic_name__     (		nn.Module   ):





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							torch.Tensor    ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.044715 * torch.pow(snake_case_							,						3.0    ))    ))







class 							__magic_name__     (		nn.Module   ):





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





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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
