"""simple docstring"""





from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__A  =				{
    # 1536-bit
    5: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 2048-bit
    14: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AACAA68FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 3072-bit
    15: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 4096-bit
    16: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
            + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
            + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
            + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
            + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
            + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'''
            + '''FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 6144-bit
    17: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'''
            + '''8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'''
            + '''302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'''
            + '''A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'''
            + '''49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'''
            + '''FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'''
            + '''180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'''
            + '''3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'''
            + '''04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'''
            + '''B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'''
            + '''1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'''
            + '''E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'''
            + '''99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'''
            + '''04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'''
            + '''233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'''
            + '''D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
            + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'''
            + '''AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'''
            + '''DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'''
            + '''2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'''
            + '''F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'''
            + '''BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
            + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'''
            + '''B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'''
            + '''387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'''
            + '''6DCC4024FFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
    # 8192-bit
    18: {
        '''prime''': int(
            '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
            + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
            + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
            + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
            + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
            + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
            + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
            + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
            + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
            + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
            + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
            + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
            + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
            + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
            + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
            + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
            + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
            + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
            + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
            + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
            + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
            + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'''
            + '''F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'''
            + '''179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'''
            + '''DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'''
            + '''5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'''
            + '''D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'''
            + '''23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
            + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'''
            + '''06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'''
            + '''DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'''
            + '''12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'''
            + '''38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'''
            + '''741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'''
            + '''3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'''
            + '''22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'''
            + '''4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'''
            + '''062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'''
            + '''4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'''
            + '''B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'''
            + '''4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'''
            + '''9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'''
            + '''60C980DD98EDD3DFFFFFFFFFFFFFFFFF''',
            base=16,
        ),
        '''generator''': 2,
    },
}
class  _snake_case     :






						def __init__(       self      :			Any							,					UpperCAmelCase      :			int = 14    ):
													if group not in primes:
																				raise ValueError("Unsupported Group"    )
													__lowerCamelCase					:		int		   =     primes[group]["prime"]
													__lowerCamelCase					:		str		   =     primes[group]["generator"]

													__lowerCamelCase					:		Optional[Any]		   =     int(hexlify(urandom(32    )    )							,					base=16    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													return hex(self.__private_key    )[2:]






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Any		   =     pow(self.generator							,					self.__private_key							,					self.prime    )
													return hex(UpperCAmelCase    )[2:]






						def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			int    ):
													# check if the other public key is valid based on NIST SP800-56
													return (
													    2 <= key <= self.prime - 2
													    and pow(UpperCAmelCase							,					(self.prime - 1) // 2							,					self.prime    ) == 1
													)






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase					:		Optional[int]		   =     int(UpperCAmelCase							,					base=16    )
													if not self.is_valid_public_key(UpperCAmelCase    ):
																				raise ValueError("Invalid public key"    )
													__lowerCamelCase					:		int		   =     pow(UpperCAmelCase							,					self.__private_key							,					self.prime    )
													return shaaaa(str(UpperCAmelCase    ).encode()    ).hexdigest()






						@staticmethod
						def       lowerCamelCase__     (       UpperCAmelCase      :			int							,					UpperCAmelCase      :			int    ):
													# check if the other public key is valid based on NIST SP800-56
													return (
													    2 <= remote_public_key_str <= prime - 2
													    and pow(UpperCAmelCase							,					(prime - 1) // 2							,					UpperCAmelCase    ) == 1
													)







						@staticmethod
						def       lowerCamelCase__     (       UpperCAmelCase      :			str							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int = 14    ):
													__lowerCamelCase					:		Optional[Any]		   =     int(UpperCAmelCase							,					base=16    )
													__lowerCamelCase					:		Tuple		   =     int(UpperCAmelCase							,					base=16    )
													__lowerCamelCase					:		List[Any]		   =     primes[group]["prime"]
													if not DiffieHellman.is_valid_public_key_static(UpperCAmelCase							,					UpperCAmelCase    ):
																				raise ValueError("Invalid public key"    )
													__lowerCamelCase					:		Optional[Any]		   =     pow(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )
													return shaaaa(str(UpperCAmelCase    ).encode()    ).hexdigest()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
"""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  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     nn.Sequential(
													    nn.Linear(UpperCAmelCase							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					nn.Linear(d_model * 4							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					)

													__lowerCamelCase					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

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

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

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

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[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(UpperCAmelCase    ).unsqueeze(1    )

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

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

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.continuous_inputs_projection(UpperCAmelCase    )
													inputs += position_encodings
													__lowerCamelCase					:		List[Any]		   =     self.dropout(UpperCAmelCase    )

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

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

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

													for lyr in self.decoders:
																				__lowerCamelCase					:		List[Any]		   =     lyr(
																				    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					)[0]

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     nn.ModuleList()

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

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

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







						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													if encoder_hidden_states is not None:
																				__lowerCamelCase					:		Tuple		   =     torch.where(encoder_attention_mask > 0							,					0							,					-1E10    ).to(
																				    encoder_hidden_states.dtype    )

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

													if conditioning_emb is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.FiLMLayer(UpperCAmelCase							,					UpperCAmelCase    )

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Parameter(torch.ones(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     eps







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any    ):
													# 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					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

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

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x