





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)
class      UpperCamelCase_						:




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=13			,  lowerCAmelCase_=7			,  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_=3			,  lowerCAmelCase_=4			,  lowerCAmelCase_=None			,  )						->							int:
										_snake_case													=			parent
										_snake_case													=			batch_size
										_snake_case													=			seq_length
										_snake_case													=			is_training
										_snake_case													=			use_token_type_ids
										_snake_case													=			use_labels
										_snake_case													=			vocab_size
										_snake_case													=			hidden_size
										_snake_case													=			num_hidden_layers
										_snake_case													=			num_attention_heads
										_snake_case													=			intermediate_size
										_snake_case													=			hidden_act
										_snake_case													=			hidden_dropout_prob
										_snake_case													=			attention_probs_dropout_prob
										_snake_case													=			max_position_embeddings
										_snake_case													=			type_vocab_size
										_snake_case													=			type_sequence_label_size
										_snake_case													=			initializer_range
										_snake_case													=			num_labels
										_snake_case													=			num_choices
										_snake_case													=			scope
										_snake_case													=			self.vocab_size - 1




					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size					)

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

										_snake_case													=			None
										_snake_case													=			None
										_snake_case													=			None
										if self.use_labels:
															_snake_case													=			ids_tensor([self.batch_size]			,  self.type_sequence_label_size					)
															_snake_case													=			ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels					)
															_snake_case													=			ids_tensor([self.batch_size]			,  self.num_choices					)

										_snake_case													=			OpenAIGPTConfig(
										    vocab_size=self.vocab_size			,  n_embd=self.hidden_size			,  n_layer=self.num_hidden_layers			,  n_head=self.num_attention_heads			,  n_positions=self.max_position_embeddings			,  pad_token_id=self.pad_token_id			,  )

										_snake_case													=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]			,  2					)

										return (
										    config,
										    input_ids,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  *lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			OpenAIGPTModel(config=lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.eval()

										_snake_case													=			model(lowerCAmelCase_			,  token_type_ids=lowerCAmelCase_			,  head_mask=lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_			,  token_type_ids=lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_					)

										self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  *lowerCAmelCase_					)						->							Dict:
										_snake_case													=			OpenAIGPTLMHeadModel(lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.eval()

										_snake_case													=			model(lowerCAmelCase_			,  token_type_ids=lowerCAmelCase_			,  labels=lowerCAmelCase_					)
										self.parent.assertEqual(result.loss.shape			,  ()					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  *lowerCAmelCase_					)						->							List[str]:
										_snake_case													=			OpenAIGPTDoubleHeadsModel(lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.eval()

										_snake_case													=			model(lowerCAmelCase_			,  token_type_ids=lowerCAmelCase_			,  labels=lowerCAmelCase_					)
										self.parent.assertEqual(result.loss.shape			,  ()					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  *lowerCAmelCase_					)						->							Dict:
										_snake_case													=			self.num_labels
										_snake_case													=			OpenAIGPTForSequenceClassification(lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.eval()

										_snake_case													=			ids_tensor([self.batch_size]			,  self.type_sequence_label_size					)
										_snake_case													=			model(lowerCAmelCase_			,  token_type_ids=lowerCAmelCase_			,  labels=lowerCAmelCase_					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)					)






					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.prepare_config_and_inputs()
										(
										    (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,       (
										    _snake_case       
										)							,              
										)													=			config_and_inputs
										_snake_case													=			{
										    'input_ids': input_ids,
										    'token_type_ids': token_type_ids,
										    'head_mask': head_mask,
										}

										return config, inputs_dict





@require_torch
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		(
					    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_							=		(
					    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
					lowerCAmelCase_							=		(
					    {
					        '''feature-extraction''': OpenAIGPTModel,
					        '''text-classification''': OpenAIGPTForSequenceClassification,
					        '''text-generation''': OpenAIGPTLMHeadModel,
					        '''zero-shot''': OpenAIGPTForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
															# tiny config could not be created.
															return True

										return False




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							Union[str, Any]:
										_snake_case													=			super()._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_			,  return_labels=lowerCAmelCase_					)

										if return_labels:
															if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																				_snake_case													=			torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)			,  dtype=torch.long			,  device=lowerCAmelCase_			,  )
																				_snake_case													=			inputs_dict['labels']
																				_snake_case													=			inputs_dict['labels']
																				_snake_case													=			torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.num_choices)			,  dtype=torch.long			,  device=lowerCAmelCase_			,  )
																				_snake_case													=			torch.zeros(
																				    self.model_tester.batch_size			,  dtype=torch.long			,  device=lowerCAmelCase_					)
										return inputs_dict




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			OpenAIGPTModelTester(self					)
										_snake_case													=			ConfigTester(self			,  config_class=lowerCAmelCase_			,  n_embd=37					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										self.config_tester.run_common_tests()




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_					)






					@slow
					def 		lowerCAmelCase     (						self					)						->							Dict:
										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case													=			OpenAIGPTModel.from_pretrained(lowerCAmelCase_					)
															self.assertIsNotNone(lowerCAmelCase_					)





@require_torch
class      UpperCamelCase_						(					unittest.TestCase   ):




					@slow
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'					)
										model.to(lowerCAmelCase_					)
										_snake_case													=			torch.tensor([[481, 4735, 544]]			,  dtype=torch.long			,  device=lowerCAmelCase_					)  # the president is
										_snake_case													=			[
										    481,
										    4735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    4_0477,
										    244,
										    249,
										    719,
										    881,
										    487,
										    544,
										    240,
										    244,
										    603,
										    481,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										_snake_case													=			model.generate(lowerCAmelCase_			,  do_sample=lowerCAmelCase_					)
										self.assertListEqual(output_ids[0].tolist()			,  lowerCAmelCase_					)










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   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

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

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

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

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

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

										_snake_case													=			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.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

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

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

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

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

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			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			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)
										_snake_case													=			NewGELUActivation()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
										# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
										# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
										# half-precision inputs is done in fp32

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

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

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase_			,  3.0					))					))





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			self.scale_bias(lowerCAmelCase_					)
										_snake_case							,       _snake_case													=			torch.chunk(lowerCAmelCase_			,  2			,  -1					)
										_snake_case													=			x * (1 + scale) + shift
										return x



