
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class       __A:
					SCREAMING_SNAKE_CASE			    =    BlenderbotConfig
					SCREAMING_SNAKE_CASE			    =    {}
					SCREAMING_SNAKE_CASE			    =    '''gelu'''

					def __init__(	self					:     str   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Dict=1_3   ,					__UpperCamelCase					:     Dict=7   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Dict=False   ,					__UpperCamelCase					:     int=9_9   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     Union[str, Any]=4   ,					__UpperCamelCase					:     Optional[Any]=3_7   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Union[str, Any]=0.1   ,					__UpperCamelCase					:     Dict=2_0   ,					__UpperCamelCase					:     Tuple=2   ,					__UpperCamelCase					:     Optional[int]=1   ,					__UpperCamelCase					:     str=0   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size

												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					eos_token_id
												lowerCamelCase_							  =					pad_token_id
												lowerCamelCase_							  =					bos_token_id

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length - 1]   ,					self.vocab_size   )
												lowerCamelCase_							  =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,					1   )
												lowerCamelCase_							  =					tf.concat([input_ids, eos_tensor]   ,					axis=1   )

												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

												lowerCamelCase_							  =					self.config_cls(
												    vocab_size=self.vocab_size   ,					d_model=self.hidden_size   ,					encoder_layers=self.num_hidden_layers   ,					decoder_layers=self.num_hidden_layers   ,					encoder_attention_heads=self.num_attention_heads   ,					decoder_attention_heads=self.num_attention_heads   ,					encoder_ffn_dim=self.intermediate_size   ,					decoder_ffn_dim=self.intermediate_size   ,					dropout=self.hidden_dropout_prob   ,					attention_dropout=self.attention_probs_dropout_prob   ,					max_position_embeddings=self.max_position_embeddings   ,					eos_token_ids=[2]   ,					bos_token_id=self.bos_token_id   ,					pad_token_id=self.pad_token_id   ,					decoder_start_token_id=self.pad_token_id   ,					**self.config_updates   ,					)
												lowerCamelCase_							  =					prepare_blenderbot_inputs_dict(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												return config, inputs_dict



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					TFBlenderbotModel(config=__UpperCamelCase   ).get_decoder()
												lowerCamelCase_							  =					inputs_dict["""input_ids"""]

												lowerCamelCase_							  =					input_ids[:1, :]
												lowerCamelCase_							  =					inputs_dict["""attention_mask"""][:1, :]
												lowerCamelCase_							  =					inputs_dict["""head_mask"""]
												lowerCamelCase_							  =					1

												# first forward pass
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					head_mask=__UpperCamelCase   ,					use_cache=__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_							  =					outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												lowerCamelCase_							  =					ids_tensor((self.batch_size, 3)   ,					config.vocab_size   )
												lowerCamelCase_							  =					tf.cast(ids_tensor((self.batch_size, 3)   ,					2   )   ,					tf.inta   )

												# append to next input_ids and
												lowerCamelCase_							  =					tf.concat([input_ids, next_tokens]   ,					axis=-1   )
												lowerCamelCase_							  =					tf.concat([attention_mask, next_attn_mask]   ,					axis=-1   )

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   )[0]
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					past_key_values=__UpperCamelCase   )[0]

												self.parent.assertEqual(next_tokens.shape[1]   ,					output_from_past.shape[1]   )

												# select random slice
												lowerCamelCase_							  =					int(ids_tensor((1,)   ,					output_from_past.shape[-1]   )   )
												lowerCamelCase_							  =					output_from_no_past[:, -3:, random_slice_idx]
												lowerCamelCase_							  =					output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(__UpperCamelCase   ,					__UpperCamelCase   ,					rtol=1E-3   )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      List[str]=None			,				UpperCAmelCase__				:      Tuple=None			,				UpperCAmelCase__				:      Dict=None			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      int=None			,				)  ->    Union[str, Any]:
							if attention_mask is None:
														lowerCamelCase_							  =					tf.cast(tf.math.not_equal(UpperCAmelCase__			,				config.pad_token_id       )			,				tf.inta       )
							if decoder_attention_mask is None:
														lowerCamelCase_							  =					tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape			,				dtype=tf.inta       ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,				config.pad_token_id       )			,				tf.inta       ),
														    ]			,				axis=-1			,				)
							if head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
							if decoder_head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							if cross_attn_head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}




@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					TFBlenderbotModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												self.config_tester.run_common_tests()



					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase   )




@require_tokenizers
@require_tf
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''My friends are cool but they eat too many carbs.''']
					SCREAMING_SNAKE_CASE			    =    '''facebook/blenderbot-400M-distill'''

					@cached_property
					def    lowercase__      (	self					:     int   ):
												return BlenderbotTokenizer.from_pretrained(self.model_name   )

					@cached_property
					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
												return model



					@slow
					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.tokenizer(self.src_text   ,					return_tensors="""tf"""   )
												lowerCamelCase_							  =					self.model.generate(
												    model_inputs.input_ids   ,					)
												lowerCamelCase_							  =					self.tokenizer.batch_decode(generated_ids.numpy()   ,					skip_special_tokens=__UpperCamelCase   )[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)







import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

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

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												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(__UpperCamelCase   ).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(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

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

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.ModuleList()

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

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

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



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												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](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

												return (hidden_states,)





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

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

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# 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

												lowerCamelCase_							  =					hidden_states.to(torch.floataa   ).pow(2   ).mean(-1   ,					keepdim=__UpperCamelCase   )
												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       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





