'''simple docstring'''

import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=13       ,lowerCamelCase_=7       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=99       ,lowerCamelCase_=32       ,lowerCamelCase_=5       ,lowerCamelCase_=4       ,lowerCamelCase_=37       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=16       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=False       ,lowerCamelCase_=True       ,lowerCamelCase_="None"       ,lowerCamelCase_=3       ,lowerCamelCase_=4       ,lowerCamelCase_=None       ,)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	parent
         UpperCAmelCase__				:					str 					=	batch_size
         UpperCAmelCase__				:					List[str] 					=	seq_length
         UpperCAmelCase__				:					str 					=	is_training
         UpperCAmelCase__				:					Optional[Any] 					=	use_input_mask
         UpperCAmelCase__				:					Any 					=	use_token_type_ids
         UpperCAmelCase__				:					Any 					=	use_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	vocab_size
         UpperCAmelCase__				:					Dict 					=	hidden_size
         UpperCAmelCase__				:					str 					=	num_hidden_layers
         UpperCAmelCase__				:					List[Any] 					=	num_attention_heads
         UpperCAmelCase__				:					Dict 					=	intermediate_size
         UpperCAmelCase__				:					List[str] 					=	hidden_act
         UpperCAmelCase__				:					Optional[int] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Optional[int] 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					List[str] 					=	max_position_embeddings
         UpperCAmelCase__				:					int 					=	type_vocab_size
         UpperCAmelCase__				:					Optional[int] 					=	type_sequence_label_size
         UpperCAmelCase__				:					Optional[int] 					=	initializer_range
         UpperCAmelCase__				:					List[str] 					=	num_labels
         UpperCAmelCase__				:					Dict 					=	num_choices
         UpperCAmelCase__				:					Dict 					=	relative_attention
         UpperCAmelCase__				:					str 					=	position_biased_input
         UpperCAmelCase__				:					Dict 					=	pos_att_type
         UpperCAmelCase__				:					int 					=	scope




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size				)

         UpperCAmelCase__				:					Any 					=	None
         if self.use_input_mask:
            UpperCAmelCase__				:					Optional[Any] 					=	ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2				)

         UpperCAmelCase__				:					Any 					=	None
         if self.use_token_type_ids:
            UpperCAmelCase__				:					str 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size				)

         UpperCAmelCase__				:					str 					=	None
         UpperCAmelCase__				:					Any 					=	None
         UpperCAmelCase__				:					str 					=	None
         if self.use_labels:
            UpperCAmelCase__				:					Dict 					=	ids_tensor([self.batch_size]       ,self.type_sequence_label_size				)
            UpperCAmelCase__				:					Tuple 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels				)
            UpperCAmelCase__				:					Optional[Any] 					=	ids_tensor([self.batch_size]       ,self.num_choices				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




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



         '''simple docstring'''



         return DebertaConfig(
             vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,relative_attention=self.relative_attention       ,position_biased_input=self.position_biased_input       ,pos_att_type=self.pos_att_type       ,)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.get_config()
         UpperCAmelCase__				:					Tuple 					=	300
         return config




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         self.parent.assertListEqual(list(result.loss.size()				)       ,[]				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	DebertaModel(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					List[Any] 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_				)[0]
         UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_       ,token_type_ids=lowerCamelCase_				)[0]
         UpperCAmelCase__				:					int 					=	model(lowerCamelCase_				)[0]

         self.parent.assertListEqual(list(sequence_output.size()				)       ,[self.batch_size, self.seq_length, self.hidden_size]				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	DebertaForMaskedLM(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					str 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,labels=lowerCamelCase_				)

         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_labels
         UpperCAmelCase__				:					Optional[Any] 					=	DebertaForSequenceClassification(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Optional[Any] 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertListEqual(list(result.logits.size()				)       ,[self.batch_size, self.num_labels]				)
         self.check_loss_output(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.num_labels
         UpperCAmelCase__				:					Optional[int] 					=	DebertaForTokenClassification(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	DebertaForQuestionAnswering(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					str 					=	model(
             lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,start_positions=lowerCamelCase_       ,end_positions=lowerCamelCase_       ,)
         self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length)				)
         self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length)				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   
         )				:					List[str] 					=	config_and_inputs
         UpperCAmelCase__				:					Optional[int] 					=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict




@require_torch
class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Optional[int]   				= (
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_   :   int   				= (
          {
              '''feature-extraction''': DebertaModel,
              '''fill-mask''': DebertaForMaskedLM,
              '''question-answering''': DebertaForQuestionAnswering,
              '''text-classification''': DebertaForSequenceClassification,
              '''token-classification''': DebertaForTokenClassification,
              '''zero-shot''': DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_   :   Optional[Any]   				= True
      UpperCAmelCase_   :   Dict   				= False
      UpperCAmelCase_   :   Optional[int]   				= False
      UpperCAmelCase_   :   Optional[int]   				= False
      UpperCAmelCase_   :   int   				= False




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	DebertaModelTester(self				)
         UpperCAmelCase__				:					Any 					=	ConfigTester(self       ,config_class=lowerCamelCase_       ,hidden_size=37				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         self.config_tester.run_common_tests()




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase_				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase__				:					Optional[Any] 					=	DebertaModel.from_pretrained(lowerCamelCase_				)
            self.assertIsNotNone(lowerCamelCase_				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @unittest.skip(reason='''Model not available yet'''				)
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         pass




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	DebertaModel.from_pretrained('''microsoft/deberta-base'''				)

         UpperCAmelCase__				:					int 					=	torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
         UpperCAmelCase__				:					List[str] 					=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
         with torch.no_grad():
            UpperCAmelCase__				:					Optional[int] 					=	model(lowerCamelCase_       ,attention_mask=lowerCamelCase_				)[0]
         # compare the actual values for a slice.
         UpperCAmelCase__				:					List[str] 					=	torch.tensor(
             [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]				)
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,lowerCamelCase_       ,atol=1e-4				)       ,f'''{output[:, 1:4, 1:4]}'''				)





'''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       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Sequential(
             nn.Linear(lowerCamelCase_       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,nn.Linear(d_model * 4       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,)

         UpperCAmelCase__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

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

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	torch.mul(query_input.unsqueeze(-1				)       ,key_input.unsqueeze(-2				)				)
         return mask.unsqueeze(-3				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

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

         UpperCAmelCase__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

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

         UpperCAmelCase__				:					List[Any] 					=	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.
         UpperCAmelCase__				:					int 					=	torch.broadcast_to(
             torch.arange(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

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

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

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

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__				:					Any 					=	torch.where(encoder_attention_mask > 0       ,0       ,-1e10				).to(
                encoder_hidden_states.dtype				)

            UpperCAmelCase__				:					Union[str, Any] 					=	self.layer[1](
                lowerCamelCase_       ,key_value_states=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[-1](lowerCamelCase_       ,lowerCamelCase_				)

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Optional[int] 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

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




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      torch.Tensor:



         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(lowerCamelCase_       ,3.0				))				))




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scale_bias(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	torch.chunk(lowerCamelCase_       ,2       ,-1				)
         UpperCAmelCase__				:					List[Any] 					=	x * (1 + scale) + shift
         return x




