



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    _A	(     __UpperCAmelCase				,__UpperCAmelCase   ):



       @register_to_config
       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					int = 128					,		__SCREAMING_SNAKE_CASE							:					int = 256					,		__SCREAMING_SNAKE_CASE							:					float = 20_00.0					,		__SCREAMING_SNAKE_CASE							:					int = 768					,		__SCREAMING_SNAKE_CASE							:					int = 12					,		__SCREAMING_SNAKE_CASE							:					int = 12					,		__SCREAMING_SNAKE_CASE							:					int = 64					,		__SCREAMING_SNAKE_CASE							:					int = 2_048					,		__SCREAMING_SNAKE_CASE							:					float = 0.1					,		):


           '''simple docstring'''

           super().__init__()

           __a							=    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()					,		)

           __a							=    nn.Embedding(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    False

           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE)

           __a							=    nn.Dropout(p=__SCREAMING_SNAKE_CASE)

           __a							=    nn.ModuleList()
           for lyr_num in range(__SCREAMING_SNAKE_CASE):
               # FiLM conditional T5 decoder
               __a							=    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)

           __a							=    TaLayerNorm(__SCREAMING_SNAKE_CASE)

           __a							=    nn.Dropout(p=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    torch.mul(query_input.unsqueeze(-1)					,		key_input.unsqueeze(-2))
           return mask.unsqueeze(-3)





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a	,       __a							=    decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

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

           __a							=    self.conditioning_emb(__SCREAMING_SNAKE_CASE).unsqueeze(1)

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

           __a							=    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.
           __a							=    torch.broadcast_to(
               torch.arange(__SCREAMING_SNAKE_CASE					,		device=decoder_input_tokens.device)					,		(batch, seq_length)					,		)

           __a							=    self.position_encoding(__SCREAMING_SNAKE_CASE)

           __a							=    self.continuous_inputs_projection(__SCREAMING_SNAKE_CASE)
           inputs += position_encodings
           __a							=    self.dropout(__SCREAMING_SNAKE_CASE)

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

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

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

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

           __a							=    self.decoder_norm(__SCREAMING_SNAKE_CASE)
           __a							=    self.post_dropout(__SCREAMING_SNAKE_CASE)

           __a							=    self.spec_out(__SCREAMING_SNAKE_CASE)
           return spec_out


class    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=1E-6):


           '''simple docstring'''

           super().__init__()
           __a							=    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 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		):


           '''simple docstring'''

           __a							=    self.layer[0](
               __SCREAMING_SNAKE_CASE					,		conditioning_emb=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		)

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

               __a							=    self.layer[1](
                   __SCREAMING_SNAKE_CASE					,		key_value_states=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		)

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

           return (hidden_states,)


class    _A	(     nn.Module   ):



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           super().__init__()
           __a							=    TaLayerNorm(__SCREAMING_SNAKE_CASE)
           __a							=    TaFiLMLayer(in_features=d_model * 4					,		out_features=__SCREAMING_SNAKE_CASE)
           __a							=    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)
           __a							=    nn.Dropout(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]=None					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		):


           '''simple docstring'''

           __a							=    self.layer_norm(__SCREAMING_SNAKE_CASE)

           if conditioning_emb is not None:
               __a							=    self.FiLMLayer(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           # Self-attention block
           __a							=    self.attention(__SCREAMING_SNAKE_CASE)

           __a							=    hidden_states + self.dropout(__SCREAMING_SNAKE_CASE)

           return hidden_states


class    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           super().__init__()
           __a							=    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)
           __a							=    TaLayerNorm(__SCREAMING_SNAKE_CASE					,		eps=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Dropout(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		):


           '''simple docstring'''

           __a							=    self.layer_norm(__SCREAMING_SNAKE_CASE)
           __a							=    self.attention(
               __SCREAMING_SNAKE_CASE					,		encoder_hidden_states=__SCREAMING_SNAKE_CASE					,		attention_mask=attention_mask.squeeze(1)					,		)
           __a							=    hidden_states + self.dropout(__SCREAMING_SNAKE_CASE)
           return layer_output


class    _A	(     nn.Module   ):



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


           '''simple docstring'''

           super().__init__()
           __a							=    TaDenseGatedActDense(d_model=__SCREAMING_SNAKE_CASE					,		d_ff=__SCREAMING_SNAKE_CASE					,		dropout_rate=__SCREAMING_SNAKE_CASE)
           __a							=    TaFiLMLayer(in_features=d_model * 4					,		out_features=__SCREAMING_SNAKE_CASE)
           __a							=    TaLayerNorm(__SCREAMING_SNAKE_CASE					,		eps=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Dropout(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __a							=    self.layer_norm(__SCREAMING_SNAKE_CASE)
           if conditioning_emb is not None:
               __a							=    self.film(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           __a							=    self.DenseReluDense(__SCREAMING_SNAKE_CASE)
           __a							=    hidden_states + self.dropout(__SCREAMING_SNAKE_CASE)
           return hidden_states


class    _A	(     nn.Module   ):



       def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__()
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Dropout(__SCREAMING_SNAKE_CASE)
           __a							=    NewGELUActivation()





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    self.act(self.wi_a(__SCREAMING_SNAKE_CASE))
           __a							=    self.wi_a(__SCREAMING_SNAKE_CASE)
           __a							=    hidden_gelu * hidden_linear
           __a							=    self.dropout(__SCREAMING_SNAKE_CASE)

           __a							=    self.wo(__SCREAMING_SNAKE_CASE)
           return hidden_states


class    _A	(     nn.Module   ):



       def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int=1E-6):


           '''simple docstring'''

           super().__init__()
           __a							=    nn.Parameter(torch.ones(__SCREAMING_SNAKE_CASE))
           __a							=    eps





       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

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

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

           return self.weight * hidden_states


class    _A	(     nn.Module   ):



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(__SCREAMING_SNAKE_CASE					,		3.0))))


class    _A	(     nn.Module   ):



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           super().__init__()
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		out_features * 2					,		bias=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    self.scale_bias(__SCREAMING_SNAKE_CASE)
           __a	,       __a							=    torch.chunk(__SCREAMING_SNAKE_CASE					,		2					,		-1)
           __a							=    x * (1 + scale) + shift
           return x










import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:int						=					logging.get_logger(__name__)

class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    question_encoder
           __a							=    generator
           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''question_encoder_tokenizer''')
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a							=    kwargs.pop('''config'''					,		__SCREAMING_SNAKE_CASE)

           if config is None:
               __a							=    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.question_encoder					,		subfolder='''question_encoder_tokenizer''')
           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.generator					,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					Dict					,		*__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		*__SCREAMING_SNAKE_CASE							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.generator





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "longest"					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''					,		__SCREAMING_SNAKE_CASE					,		)
           if max_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               __SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               text_target=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    labels['''input_ids''']
           return model_inputs





