



"""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 			lowerCAmelCase_		(__A				,      __A						):


				"""simple docstring"""



				@register_to_config
				def __init__(self     , SCREAMING_SNAKE_CASE__ = 1_28     , SCREAMING_SNAKE_CASE__ = 2_56     , SCREAMING_SNAKE_CASE__ = 20_00.0     , SCREAMING_SNAKE_CASE__ = 7_68     , SCREAMING_SNAKE_CASE__ = 12     , SCREAMING_SNAKE_CASE__ = 12     , SCREAMING_SNAKE_CASE__ = 64     , SCREAMING_SNAKE_CASE__ = 20_48     , SCREAMING_SNAKE_CASE__ = 0.1     , )      ->	int:



											"""simple docstring"""




											super().__init__()

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     nn.Sequential(
											    nn.Linear(SCREAMING_SNAKE_CASE__     , d_model * 4     , bias=SCREAMING_SNAKE_CASE__					)     , nn.SiLU()     , nn.Linear(d_model * 4     , d_model * 4     , bias=SCREAMING_SNAKE_CASE__					)     , nn.SiLU()     , )

											SCREAMING_SNAKE_CASE__		: Any     =     nn.Embedding(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     False

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

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     nn.Dropout(p=SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     TaLayerNorm(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     nn.Dropout(p=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     nn.Linear(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , bias=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     torch.mul(query_input.unsqueeze(-1					)     , key_input.unsqueeze(-2					)					)
											return mask.unsqueeze(-3					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

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

											SCREAMING_SNAKE_CASE__		: int     =     self.conditioning_emb(SCREAMING_SNAKE_CASE__					).unsqueeze(1					)

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

											SCREAMING_SNAKE_CASE__		: 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.
											SCREAMING_SNAKE_CASE__		: str     =     torch.broadcast_to(
											    torch.arange(SCREAMING_SNAKE_CASE__     , device=decoder_input_tokens.device					)     , (batch, seq_length)     , )

											SCREAMING_SNAKE_CASE__		: Tuple     =     self.position_encoding(SCREAMING_SNAKE_CASE__					)

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

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

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

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

											for lyr in self.decoders:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     lyr(
																		    SCREAMING_SNAKE_CASE__     , conditioning_emb=SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , encoder_attention_mask=SCREAMING_SNAKE_CASE__     , )[0]

											SCREAMING_SNAKE_CASE__		: Any     =     self.decoder_norm(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.post_dropout(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     self.spec_out(SCREAMING_SNAKE_CASE__					)
											return spec_out






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=1E-6					)      ->	Any:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: str     =     nn.ModuleList()

											# cond self attention: layer 0
											self.layer.append(
											    TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE__     , d_kv=SCREAMING_SNAKE_CASE__     , num_heads=SCREAMING_SNAKE_CASE__     , dropout_rate=SCREAMING_SNAKE_CASE__					)					)

											# cross attention: layer 1
											self.layer.append(
											    TaLayerCrossAttention(
											        d_model=SCREAMING_SNAKE_CASE__     , d_kv=SCREAMING_SNAKE_CASE__     , num_heads=SCREAMING_SNAKE_CASE__     , dropout_rate=SCREAMING_SNAKE_CASE__     , layer_norm_epsilon=SCREAMING_SNAKE_CASE__     , )					)

											# Film Cond MLP + dropout: last layer
											self.layer.append(
											    TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE__     , d_ff=SCREAMING_SNAKE_CASE__     , dropout_rate=SCREAMING_SNAKE_CASE__     , layer_norm_epsilon=SCREAMING_SNAKE_CASE__					)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , )      ->	Tuple:



											"""simple docstring"""




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

											if encoder_hidden_states is not None:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.where(encoder_attention_mask > 0     , 0     , -1E10					).to(
																		    encoder_hidden_states.dtype					)

																		SCREAMING_SNAKE_CASE__		: List[str]     =     self.layer[1](
																		    SCREAMING_SNAKE_CASE__     , key_value_states=SCREAMING_SNAKE_CASE__     , attention_mask=SCREAMING_SNAKE_CASE__     , )

											# Apply Film Conditional Feed Forward layer
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.layer[-1](SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											return (hidden_states,)






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , )      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.layer_norm(SCREAMING_SNAKE_CASE__					)

											if conditioning_emb is not None:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     self.FiLMLayer(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											# Self-attention block
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.attention(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_states + self.dropout(SCREAMING_SNAKE_CASE__					)

											return hidden_states






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: str     =     Attention(query_dim=SCREAMING_SNAKE_CASE__     , heads=SCREAMING_SNAKE_CASE__     , dim_head=SCREAMING_SNAKE_CASE__     , out_bias=SCREAMING_SNAKE_CASE__     , scale_qk=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     TaLayerNorm(SCREAMING_SNAKE_CASE__     , eps=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     nn.Dropout(SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , )      ->	int:



											"""simple docstring"""




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






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: List[str]     =     TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE__     , d_ff=SCREAMING_SNAKE_CASE__     , dropout_rate=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     TaFiLMLayer(in_features=d_model * 4     , out_features=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     TaLayerNorm(SCREAMING_SNAKE_CASE__     , eps=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     nn.Dropout(SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.layer_norm(SCREAMING_SNAKE_CASE__					)
											if conditioning_emb is not None:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     self.film(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

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






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: List[str]     =     nn.Linear(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , bias=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     nn.Linear(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , bias=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     nn.Linear(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , bias=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     nn.Dropout(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     NewGELUActivation()



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     self.act(self.wi_a(SCREAMING_SNAKE_CASE__					)					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.wi_a(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_gelu * hidden_linear
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.dropout(SCREAMING_SNAKE_CASE__					)

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






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=1E-6					)      ->	Optional[int]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: str     =     nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE__					)					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     eps



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_states.to(torch.floataa					).pow(2					).mean(-1     , keepdim=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

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

											return self.weight * hidden_states






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	torch.Tensor:



											"""simple docstring"""




											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.044715 * torch.pow(SCREAMING_SNAKE_CASE__     , 3.0					))					))






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Any     =     nn.Linear(SCREAMING_SNAKE_CASE__     , out_features * 2     , bias=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.scale_bias(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     torch.chunk(SCREAMING_SNAKE_CASE__     , 2     , -1					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     x * (1 + scale) + shift
											return x






"""simple docstring"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




