





'''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 							__UpperCAmelCase     (				__lowerCAmelCase						,    __lowerCAmelCase  ):







		'''simple docstring'''







		@register_to_config
		def __init__(self  :					Dict					,					_lowerCAmelCase  :					int = 128					,					_lowerCAmelCase  :					Tuple = 256					,					_lowerCAmelCase  :					Union[str, Any] = 2_000.0					,					_lowerCAmelCase  :					List[Any] = 768					,					_lowerCAmelCase  :					Union[str, Any] = 12					,					_lowerCAmelCase  :					Dict = 12					,					_lowerCAmelCase  :					List[Any] = 64					,					_lowerCAmelCase  :					Optional[Any] = 2048					,					_lowerCAmelCase  :					List[Any] = 0.1					,					):
			super().__init__()

			A							     =					nn.Sequential(
			    nn.Linear(_lowerCAmelCase					,					d_model * 4					,					bias=_lowerCAmelCase					)					,					nn.SiLU()					,					nn.Linear(d_model * 4					,					d_model * 4					,					bias=_lowerCAmelCase					)					,					nn.SiLU()					,					)

			A							     =					nn.Embedding(_lowerCAmelCase					,					_lowerCAmelCase					)
			A							     =					False

			A							     =					nn.Linear(_lowerCAmelCase					,					_lowerCAmelCase					,					bias=_lowerCAmelCase					)

			A							     =					nn.Dropout(p=_lowerCAmelCase					)

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

			A							     =					TaLayerNorm(_lowerCAmelCase					)

			A							     =					nn.Dropout(p=_lowerCAmelCase					)
			A							     =					nn.Linear(_lowerCAmelCase					,					_lowerCAmelCase					,					bias=_lowerCAmelCase					)







		def 				A   (self  :					List[Any]					,					_lowerCAmelCase  :					Union[str, Any]					,					_lowerCAmelCase  :					Optional[Any]					):
			A							     =					torch.mul(query_input.unsqueeze(-1					)					,					key_input.unsqueeze(-2					)					)
			return mask.unsqueeze(-3					)







		def 				A   (self  :					Union[str, Any]					,					_lowerCAmelCase  :					Dict					,					_lowerCAmelCase  :					Dict					,					_lowerCAmelCase  :					Optional[Any]					):
			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(_lowerCAmelCase					).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(_lowerCAmelCase					,					device=decoder_input_tokens.device					)					,					(batch, seq_length)					,					)

			A							     =					self.position_encoding(_lowerCAmelCase					)

			A							     =					self.continuous_inputs_projection(_lowerCAmelCase					)
			inputs += position_encodings
			A							     =					self.dropout(_lowerCAmelCase					)

			# 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(_lowerCAmelCase					,					_lowerCAmelCase					)) 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(
				    _lowerCAmelCase					,					conditioning_emb=_lowerCAmelCase					,					encoder_hidden_states=_lowerCAmelCase					,					encoder_attention_mask=_lowerCAmelCase					,					)[0]

			A							     =					self.decoder_norm(_lowerCAmelCase					)
			A							     =					self.post_dropout(_lowerCAmelCase					)

			A							     =					self.spec_out(_lowerCAmelCase					)
			return spec_out






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







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

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

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

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







		def 				A   (self  :					Union[str, Any]					,					_lowerCAmelCase  :					List[str]					,					_lowerCAmelCase  :					Optional[int]=None					,					_lowerCAmelCase  :					int=None					,					_lowerCAmelCase  :					List[str]=None					,					_lowerCAmelCase  :					Optional[int]=None					,					_lowerCAmelCase  :					List[Any]=None					,					):
			A							     =					self.layer[0](
			    _lowerCAmelCase					,					conditioning_emb=_lowerCAmelCase					,					attention_mask=_lowerCAmelCase					,					)

			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](
				    _lowerCAmelCase					,					key_value_states=_lowerCAmelCase					,					attention_mask=_lowerCAmelCase					,					)

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

			return (hidden_states,)






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







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







		def 				A   (self  :					List[str]					,					_lowerCAmelCase  :					List[Any]					,					_lowerCAmelCase  :					Optional[int]=None					,					_lowerCAmelCase  :					Any=None					,					):
			A							     =					self.layer_norm(_lowerCAmelCase					)

			if conditioning_emb is not None:
				A							     =					self.FiLMLayer(_lowerCAmelCase					,					_lowerCAmelCase					)

			# Self-attention block
			A							     =					self.attention(_lowerCAmelCase					)

			A							     =					hidden_states + self.dropout(_lowerCAmelCase					)

			return hidden_states






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







		def __init__(self  :					Dict					,					_lowerCAmelCase  :					List[str]					,					_lowerCAmelCase  :					int					,					_lowerCAmelCase  :					Dict					,					_lowerCAmelCase  :					Union[str, Any]					,					_lowerCAmelCase  :					Union[str, Any]					):
			super().__init__()
			A							     =					Attention(query_dim=_lowerCAmelCase					,					heads=_lowerCAmelCase					,					dim_head=_lowerCAmelCase					,					out_bias=_lowerCAmelCase					,					scale_qk=_lowerCAmelCase					)
			A							     =					TaLayerNorm(_lowerCAmelCase					,					eps=_lowerCAmelCase					)
			A							     =					nn.Dropout(_lowerCAmelCase					)







		def 				A   (self  :					Any					,					_lowerCAmelCase  :					Optional[Any]					,					_lowerCAmelCase  :					Optional[int]=None					,					_lowerCAmelCase  :					Any=None					,					):
			A							     =					self.layer_norm(_lowerCAmelCase					)
			A							     =					self.attention(
			    _lowerCAmelCase					,					encoder_hidden_states=_lowerCAmelCase					,					attention_mask=attention_mask.squeeze(1					)					,					)
			A							     =					hidden_states + self.dropout(_lowerCAmelCase					)
			return layer_output






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







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







		def 				A   (self  :					Tuple					,					_lowerCAmelCase  :					Union[str, Any]					,					_lowerCAmelCase  :					Any=None					):
			A							     =					self.layer_norm(_lowerCAmelCase					)
			if conditioning_emb is not None:
				A							     =					self.film(_lowerCAmelCase					,					_lowerCAmelCase					)

			A							     =					self.DenseReluDense(_lowerCAmelCase					)
			A							     =					hidden_states + self.dropout(_lowerCAmelCase					)
			return hidden_states






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







		def __init__(self  :					List[str]					,					_lowerCAmelCase  :					Dict					,					_lowerCAmelCase  :					List[Any]					,					_lowerCAmelCase  :					List[str]					):
			super().__init__()
			A							     =					nn.Linear(_lowerCAmelCase					,					_lowerCAmelCase					,					bias=_lowerCAmelCase					)
			A							     =					nn.Linear(_lowerCAmelCase					,					_lowerCAmelCase					,					bias=_lowerCAmelCase					)
			A							     =					nn.Linear(_lowerCAmelCase					,					_lowerCAmelCase					,					bias=_lowerCAmelCase					)
			A							     =					nn.Dropout(_lowerCAmelCase					)
			A							     =					NewGELUActivation()







		def 				A   (self  :					Optional[int]					,					_lowerCAmelCase  :					Tuple					):
			A							     =					self.act(self.wi_a(_lowerCAmelCase					)					)
			A							     =					self.wi_a(_lowerCAmelCase					)
			A							     =					hidden_gelu * hidden_linear
			A							     =					self.dropout(_lowerCAmelCase					)

			A							     =					self.wo(_lowerCAmelCase					)
			return hidden_states






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







		def __init__(self  :					Optional[Any]					,					_lowerCAmelCase  :					Dict					,					_lowerCAmelCase  :					Optional[Any]=1e-6					):
			super().__init__()
			A							     =					nn.Parameter(torch.ones(_lowerCAmelCase					)					)
			A							     =					eps







		def 				A   (self  :					List[str]					,					_lowerCAmelCase  :					Dict					):
			A							     =					hidden_states.to(torch.floataa					).pow(2					).mean(-1					,					keepdim=_lowerCAmelCase					)
			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 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







		def 				A   (self  :					Dict					,					_lowerCAmelCase  :					Tuple					):
			return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.044_715 * torch.pow(_lowerCAmelCase					,					3.0					))					))






class 							__UpperCAmelCase     (				nn.Module  ):







		'''simple docstring'''







		def __init__(self  :					Any					,					_lowerCAmelCase  :					str					,					_lowerCAmelCase  :					Dict					):
			super().__init__()
			A							     =					nn.Linear(_lowerCAmelCase					,					out_features * 2					,					bias=_lowerCAmelCase					)







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


"""simple docstring"""
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





        if index is not None and index.is_initialized() and len(lowercase) > 0:
          raise ValueError(
              'When using Ray for distributed fine-tuning, '
              'you\'ll need to provide the paths instead, '
              'as the dataset and the index are loaded '
              'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
        super().__init__(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
        a__:     Any										=	retrieval_workers
        if len(self.retrieval_workers) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)
                  for worker in self.retrieval_workers
              ])



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        logger.info('initializing retrieval')

        if len(self.retrieval_workers) > 0:
          ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
        else:
          # Non-distributed training. Load index into this same process.
          self.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        if len(self.retrieval_workers) > 0:
          # Select a random retrieval actor.
          a__:     int										=	self.retrieval_workers[random.randint(0    ,			len(self.retrieval_workers) - 1)]
          a__							,    a__:     List[Any]										=	ray.get(random_worker.retrieve.remote(lowercase    ,			lowercase))
        else:
          a__							,    a__:     Dict										=	self._main_retrieve(lowercase    ,			lowercase)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        return super(lowercase    ,			cls).get_tokenizers(lowercase    ,			lowercase    ,			**lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.pop('config'    ,			lowercase) or RagConfig.from_pretrained(lowercase    ,			**lowercase)
        a__:     Union[str, Any]										=	RagTokenizer.from_pretrained(lowercase    ,			config=lowercase)
        a__:     int										=	rag_tokenizer.question_encoder
        a__:     Any										=	rag_tokenizer.generator
        if indexed_dataset is not None:
          a__:     List[Any]										=	'custom'
          a__:     Optional[Any]										=	CustomHFIndex(config.retrieval_vector_size    ,			lowercase)
        else:
          a__:     Dict										=	cls._build_index(lowercase)
        return cls(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			retrieval_workers=lowercase    ,			index=lowercase    ,			)


