






'''simple docstring'''





import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


__A							:							str  							=		pytest.mark.integration
@require_faiss
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def    a__				(					self     :     str     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     Dataset.from_dict({"filename": ["my_name-train" + "_" + str(__lowerCamelCase     ) for x in np.arange(30     ).tolist()]}     )
     return dset

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

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     self._create_dummy_dataset()
     lowerCamelCase__  =     dset.map(
         lambda __lowerCamelCase				,     __lowerCamelCase     : {"vecs": i * np.ones(5				,     dtype=np.floataa     )}				,     with_indices=__lowerCamelCase				,     keep_in_memory=__lowerCamelCase     )
     lowerCamelCase__  =     dset.add_faiss_index("vecs"				,     batch_size=100				,     metric_type=faiss.METRIC_INNER_PRODUCT     )
     lowerCamelCase__  ,	lowerCamelCase__  =     dset.get_nearest_examples("vecs"				,     np.ones(5				,     dtype=np.floataa     )     )
     self.assertEqual(examples["filename"][0]				,     "my_name-train_29"     )
     dset.drop_index("vecs"     )

    def    a__				(					self     :     Optional[int]     )							->							List[Any]:

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1				,     1     )				,     index_name="vecs"				,     batch_size=100				,     metric_type=faiss.METRIC_INNER_PRODUCT				,     )
     lowerCamelCase__  ,	lowerCamelCase__  =     dset.get_nearest_examples("vecs"				,     np.ones(5				,     dtype=np.floataa     )     )
     self.assertEqual(examples["filename"][0]				,     "my_name-train_29"     )

    def    a__				(					self     :     str     )							->							List[Any]:

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1				,     1     )				,     index_name="vecs"				,     metric_type=faiss.METRIC_INNER_PRODUCT				,     )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=__lowerCamelCase     ) as tmp_file:
      dset.save_faiss_index("vecs"				,     tmp_file.name     )
      dset.load_faiss_index("vecs2"				,     tmp_file.name     )
     os.unlink(tmp_file.name     )

     lowerCamelCase__  ,	lowerCamelCase__  =     dset.get_nearest_examples("vecs2"				,     np.ones(5				,     dtype=np.floataa     )     )
     self.assertEqual(examples["filename"][0]				,     "my_name-train_29"     )

    def    a__				(					self     :     Any     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1				,     1     )				,     index_name="vecs"     )
     dset.drop_index("vecs"     )
     self.assertRaises(__lowerCamelCase				,     partial(dset.get_nearest_examples				,     "vecs2"				,     np.ones(5				,     dtype=np.floataa     )     )     )




    def    a__				(					self     :     int     )							->							Optional[Any]:

     '''simple docstring'''




     from elasticsearch import Elasticsearch

     lowerCamelCase__  =     self._create_dummy_dataset()
     with patch("elasticsearch.Elasticsearch.search"     ) as mocked_search, patch(
         "elasticsearch.client.IndicesClient.create"     ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"     ) as mocked_bulk:
      lowerCamelCase__  =     {"acknowledged": True}
      mocked_bulk.return_value([(True, None)] * 30     )
      lowerCamelCase__  =     {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
      lowerCamelCase__  =     Elasticsearch()

      dset.add_elasticsearch_index("filename"				,     es_client=__lowerCamelCase     )
      lowerCamelCase__  ,	lowerCamelCase__  =     dset.get_nearest_examples("filename"				,     "my_name-train_29"     )
      self.assertEqual(examples["filename"][0]				,     "my_name-train_29"     )
@require_faiss
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def    a__				(					self     :     str     )							->							Optional[int]:

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )

     # add vectors
     index.add_vectors(np.eye(5				,     dtype=np.floataa     )     )
     self.assertIsNotNone(index.faiss_index     )
     self.assertEqual(index.faiss_index.ntotal				,     5     )
     index.add_vectors(np.zeros((5, 5)				,     dtype=np.floataa     )     )
     self.assertEqual(index.faiss_index.ntotal				,     10     )

     # single query
     lowerCamelCase__  =     np.zeros(5				,     dtype=np.floataa     )
     lowerCamelCase__  =     1
     lowerCamelCase__  ,	lowerCamelCase__  =     index.search(__lowerCamelCase     )
     self.assertRaises(__lowerCamelCase				,     index.search				,     query.reshape(-1				,     1     )     )
     self.assertGreater(scores[0]				,     0     )
     self.assertEqual(indices[0]				,     1     )

     # batched queries
     lowerCamelCase__  =     np.eye(5				,     dtype=np.floataa     )[::-1]
     lowerCamelCase__  ,	lowerCamelCase__  =     index.search_batch(__lowerCamelCase     )
     self.assertRaises(__lowerCamelCase				,     index.search_batch				,     queries[0]     )
     lowerCamelCase__  =     [scores[0] for scores in total_scores]
     lowerCamelCase__  =     [indices[0] for indices in total_indices]
     self.assertGreater(np.min(__lowerCamelCase     )				,     0     )
     self.assertListEqual([4, 3, 2, 1, 0]				,     __lowerCamelCase     )

    def    a__				(					self     :     str     )							->							Any:

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     FaissIndex(string_factory="Flat"     )
     index.add_vectors(np.eye(5				,     dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index				,     faiss.IndexFlat     )
     lowerCamelCase__  =     FaissIndex(string_factory="LSH"     )
     index.add_vectors(np.eye(5				,     dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index				,     faiss.IndexLSH     )
     with self.assertRaises(__lowerCamelCase     ):
      lowerCamelCase__  =     FaissIndex(string_factory="Flat"				,     custom_index=faiss.IndexFlat(5     )     )

    def    a__				(					self     :     Dict     )							->							List[Any]:

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     faiss.IndexFlat(5     )
     lowerCamelCase__  =     FaissIndex(custom_index=__lowerCamelCase     )
     index.add_vectors(np.eye(5				,     dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index				,     faiss.IndexFlat     )




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

     '''simple docstring'''




     import faiss

     lowerCamelCase__  =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
     index.add_vectors(np.eye(5				,     dtype=np.floataa     )     )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=__lowerCamelCase     ) as tmp_file:
      index.save(tmp_file.name     )
      lowerCamelCase__  =     FaissIndex.load(tmp_file.name     )
     os.unlink(tmp_file.name     )

     lowerCamelCase__  =     np.zeros(5				,     dtype=np.floataa     )
     lowerCamelCase__  =     1
     lowerCamelCase__  ,	lowerCamelCase__  =     index.search(__lowerCamelCase     )
     self.assertGreater(scores[0]				,     0     )
     self.assertEqual(indices[0]				,     1     )






@require_faiss
def    lowerCamelCase_     (    lowercase__):
 import faiss

 lowerCamelCase__  =     FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT)
 index.add_vectors(np.eye(5				,     dtype=np.floataa))

 lowerCamelCase__  =     "index.faiss"
 lowerCamelCase__  =     F'''mock://{index_name}'''
 index.save(lowercase__				,     storage_options=mockfs.storage_options)
 lowerCamelCase__  =     FaissIndex.load(lowercase__				,     storage_options=mockfs.storage_options)

 lowerCamelCase__  =     np.zeros(5				,     dtype=np.floataa)
 lowerCamelCase__  =     1
 lowerCamelCase__  ,	lowerCamelCase__  =     index.search(lowercase__)
 assert scores[0] > 0
 assert indices[0] == 1
@require_elasticsearch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def    a__				(					self     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




     from elasticsearch import Elasticsearch

     with patch("elasticsearch.Elasticsearch.search"     ) as mocked_search, patch(
         "elasticsearch.client.IndicesClient.create"     ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"     ) as mocked_bulk:
      lowerCamelCase__  =     Elasticsearch()
      lowerCamelCase__  =     {"acknowledged": True}
      lowerCamelCase__  =     ElasticSearchIndex(es_client=__lowerCamelCase     )
      mocked_bulk.return_value([(True, None)] * 3     )
      index.add_documents(["foo", "bar", "foobar"]     )

      # single query
      lowerCamelCase__  =     "foo"
      lowerCamelCase__  =     {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
      lowerCamelCase__  ,	lowerCamelCase__  =     index.search(__lowerCamelCase     )
      self.assertEqual(scores[0]				,     1     )
      self.assertEqual(indices[0]				,     0     )

      # single query with timeout
      lowerCamelCase__  =     "foo"
      lowerCamelCase__  =     {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
      lowerCamelCase__  ,	lowerCamelCase__  =     index.search(__lowerCamelCase				,     request_timeout=30     )
      self.assertEqual(scores[0]				,     1     )
      self.assertEqual(indices[0]				,     0     )

      # batched queries
      lowerCamelCase__  =     ["foo", "bar", "foobar"]
      lowerCamelCase__  =     {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
      lowerCamelCase__  ,	lowerCamelCase__  =     index.search_batch(__lowerCamelCase     )
      lowerCamelCase__  =     [scores[0] for scores in total_scores]
      lowerCamelCase__  =     [indices[0] for indices in total_indices]
      self.assertGreater(np.min(__lowerCamelCase     )				,     0     )
      self.assertListEqual([1, 1, 1]				,     __lowerCamelCase     )

      # batched queries with timeout
      lowerCamelCase__  =     ["foo", "bar", "foobar"]
      lowerCamelCase__  =     {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
      lowerCamelCase__  ,	lowerCamelCase__  =     index.search_batch(__lowerCamelCase				,     request_timeout=30     )
      lowerCamelCase__  =     [scores[0] for scores in total_scores]
      lowerCamelCase__  =     [indices[0] for indices in total_indices]
      self.assertGreater(np.min(__lowerCamelCase     )				,     0     )
      self.assertListEqual([1, 1, 1]				,     __lowerCamelCase     )









'''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 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()

     lowerCamelCase__  =     nn.Sequential(
         nn.Linear(__lowerCamelCase				,     d_model * 4				,     bias=__lowerCamelCase     )				,     nn.SiLU()				,     nn.Linear(d_model * 4				,     d_model * 4				,     bias=__lowerCamelCase     )				,     nn.SiLU()				,     )

     lowerCamelCase__  =     nn.Embedding(__lowerCamelCase				,     __lowerCamelCase     )
     lowerCamelCase__  =     False

     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase     )

     lowerCamelCase__  =     nn.Dropout(p=__lowerCamelCase     )

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

     lowerCamelCase__  =     TaLayerNorm(__lowerCamelCase     )

     lowerCamelCase__  =     nn.Dropout(p=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase     )

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Optional[int]     )							->							Any:

     '''simple docstring'''




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




    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Union[str, Any]     )							->							Optional[int]:

     '''simple docstring'''




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

     # decoder_noise_time is in [0, 1), so rescale to expected timing range.
     lowerCamelCase__  =     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__  =     self.conditioning_emb(__lowerCamelCase     ).unsqueeze(1     )

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

     lowerCamelCase__  =     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__  =     torch.broadcast_to(
         torch.arange(__lowerCamelCase				,     device=decoder_input_tokens.device     )				,     (batch, seq_length)				,     )

     lowerCamelCase__  =     self.position_encoding(__lowerCamelCase     )

     lowerCamelCase__  =     self.continuous_inputs_projection(__lowerCamelCase     )
     inputs += position_encodings
     lowerCamelCase__  =     self.dropout(__lowerCamelCase     )

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

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

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

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

     lowerCamelCase__  =     self.decoder_norm(__lowerCamelCase     )
     lowerCamelCase__  =     self.post_dropout(__lowerCamelCase     )

     lowerCamelCase__  =     self.spec_out(__lowerCamelCase     )
     return spec_out
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     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     :     List[Any]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     Optional[int]=None				,     __lowerCamelCase     :     int=None				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     self.layer[0](
         __lowerCamelCase				,     conditioning_emb=__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     )

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

      lowerCamelCase__  =     self.layer[1](
          __lowerCamelCase				,     key_value_states=__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     )

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

     return (hidden_states,)
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     List[Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Tuple     )							->							Optional[int]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     TaLayerNorm(__lowerCamelCase     )
     lowerCamelCase__  =     TaFiLMLayer(in_features=d_model * 4				,     out_features=__lowerCamelCase     )
     lowerCamelCase__  =     Attention(query_dim=__lowerCamelCase				,     heads=__lowerCamelCase				,     dim_head=__lowerCamelCase				,     out_bias=__lowerCamelCase				,     scale_qk=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Dropout(__lowerCamelCase     )




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     str=None				,     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.layer_norm(__lowerCamelCase     )

     if conditioning_emb is not None:
      lowerCamelCase__  =     self.FiLMLayer(__lowerCamelCase				,     __lowerCamelCase     )

     # Self-attention block
     lowerCamelCase__  =     self.attention(__lowerCamelCase     )

     lowerCamelCase__  =     hidden_states + self.dropout(__lowerCamelCase     )

     return hidden_states
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     Any				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Dict     )							->							Union[str, Any]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     Attention(query_dim=__lowerCamelCase				,     heads=__lowerCamelCase				,     dim_head=__lowerCamelCase				,     out_bias=__lowerCamelCase				,     scale_qk=__lowerCamelCase     )
     lowerCamelCase__  =     TaLayerNorm(__lowerCamelCase				,     eps=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Dropout(__lowerCamelCase     )




    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     Dict=None				,     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     self.layer_norm(__lowerCamelCase     )
     lowerCamelCase__  =     self.attention(
         __lowerCamelCase				,     encoder_hidden_states=__lowerCamelCase				,     attention_mask=attention_mask.squeeze(1     )				,     )
     lowerCamelCase__  =     hidden_states + self.dropout(__lowerCamelCase     )
     return layer_output
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




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




    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]=None     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     self.layer_norm(__lowerCamelCase     )
     if conditioning_emb is not None:
      lowerCamelCase__  =     self.film(__lowerCamelCase				,     __lowerCamelCase     )

     lowerCamelCase__  =     self.DenseReluDense(__lowerCamelCase     )
     lowerCamelCase__  =     hidden_states + self.dropout(__lowerCamelCase     )
     return hidden_states
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     str     )							->							str:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase     )
     lowerCamelCase__  =     nn.Dropout(__lowerCamelCase     )
     lowerCamelCase__  =     NewGELUActivation()




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Union[str, Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.act(self.wi_a(__lowerCamelCase     )     )
     lowerCamelCase__  =     self.wi_a(__lowerCamelCase     )
     lowerCamelCase__  =     hidden_gelu * hidden_linear
     lowerCamelCase__  =     self.dropout(__lowerCamelCase     )

     lowerCamelCase__  =     self.wo(__lowerCamelCase     )
     return hidden_states
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[Any]=1E-6     )							->							Union[str, Any]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     nn.Parameter(torch.ones(__lowerCamelCase     )     )
     lowerCamelCase__  =     eps




    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     str     )							->							int:

     '''simple docstring'''




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

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

     return self.weight * hidden_states
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     torch.Tensor     )							->							torch.Tensor:

     '''simple docstring'''




     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.0_4_4_7_1_5 * torch.pow(__lowerCamelCase				,     3.0     ))     ))
class 				lowercase       (							nn.Module      ):


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     super().__init__()
     lowerCamelCase__  =     nn.Linear(__lowerCamelCase				,     out_features * 2				,     bias=__lowerCamelCase     )




    def    a__				(					self     :     str				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




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

