



"""simple docstring"""


import math
import unittest

from transformers import BioGptConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptTokenizer,
				)
				from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Tuple       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Any=13       ,  lowercase_			:List[str]=7       ,  lowercase_			:Optional[int]=True       ,  lowercase_			:int=True       ,  lowercase_			:Tuple=False       ,  lowercase_			:str=True       ,  lowercase_			:List[Any]=99       ,  lowercase_			:Dict=32       ,  lowercase_			:Optional[int]=5       ,  lowercase_			:List[str]=4       ,  lowercase_			:int=37       ,  lowercase_			:int="gelu"       ,  lowercase_			:int=0.1       ,  lowercase_			:List[str]=0.1       ,  lowercase_			:Tuple=5_12       ,  lowercase_			:Dict=16       ,  lowercase_			:str=2       ,  lowercase_			:int=0.02       ,  lowercase_			:List[Any]=3       ,  lowercase_			:List[Any]=4       ,  lowercase_			:str=None       ,  )    ->      Union[str, Any]:
												UpperCAmelCase					=			parent
												UpperCAmelCase					=			batch_size
												UpperCAmelCase					=			seq_length
												UpperCAmelCase					=			is_training
												UpperCAmelCase					=			use_input_mask
												UpperCAmelCase					=			use_token_type_ids
												UpperCAmelCase					=			use_labels
												UpperCAmelCase					=			vocab_size
												UpperCAmelCase					=			hidden_size
												UpperCAmelCase					=			num_hidden_layers
												UpperCAmelCase					=			num_attention_heads
												UpperCAmelCase					=			intermediate_size
												UpperCAmelCase					=			hidden_act
												UpperCAmelCase					=			hidden_dropout_prob
												UpperCAmelCase					=			attention_probs_dropout_prob
												UpperCAmelCase					=			max_position_embeddings
												UpperCAmelCase					=			type_vocab_size
												UpperCAmelCase					=			type_sequence_label_size
												UpperCAmelCase					=			initializer_range
												UpperCAmelCase					=			num_labels
												UpperCAmelCase					=			num_choices
												UpperCAmelCase					=			scope






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[Any]:
												UpperCAmelCase					=			ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size )

												UpperCAmelCase					=			None
												if self.use_input_mask:
																		UpperCAmelCase					=			random_attention_mask([self.batch_size, self.seq_length] )

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

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

												UpperCAmelCase					=			self.get_config()

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Dict:
												return BioGptConfig(
												    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       ,  is_decoder=lowercase_       ,  initializer_range=self.initializer_range       ,  )






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:str       ,  lowercase_			:Tuple       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:int       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			BioGptModel(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_ )
												UpperCAmelCase					=			model(lowercase_ )
												self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size) )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:List[str]       ,  lowercase_			:Dict       ,  lowercase_			:Tuple       ,  lowercase_			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Dict       ,  lowercase_			:Dict       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Union[str, Any]       ,  )    ->      List[Any]:
												UpperCAmelCase					=			BioGptForCausalLM(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  token_type_ids=lowercase_       ,  labels=lowercase_ )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size) )






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:List[Any]       ,  lowercase_			:List[str]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:str       ,  lowercase_			:Tuple       ,  *lowercase_			:List[str] )    ->      int:
												UpperCAmelCase					=			BioGptModel(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()

												# create attention mask
												UpperCAmelCase					=			torch.ones(input_ids.shape       ,  dtype=torch.long       ,  device=lowercase_ )
												UpperCAmelCase					=			self.seq_length // 2
												UpperCAmelCase					=			0

												# first forward pass
												UpperCAmelCase				,   UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_ ).to_tuple()

												# create hypothetical next token and extent to next_input_ids
												UpperCAmelCase					=			ids_tensor((self.batch_size, 1)       ,  config.vocab_size )

												# change a random masked slice from input_ids
												UpperCAmelCase					=			ids_tensor((1,)       ,  lowercase_ ).item() + 1
												UpperCAmelCase					=			ids_tensor((self.batch_size, 1)       ,  config.vocab_size ).squeeze(-1 )
												UpperCAmelCase					=			random_other_next_tokens

												# append to next input_ids and attn_mask
												UpperCAmelCase					=			torch.cat([input_ids, next_tokens]       ,  dim=-1 )
												UpperCAmelCase					=			torch.cat(
												    [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,  dtype=torch.long       ,  device=lowercase_ )]       ,  dim=1       ,  )

												# get two different outputs
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_ )['last_hidden_state']
												UpperCAmelCase					=			model(lowercase_       ,  past_key_values=lowercase_       ,  attention_mask=lowercase_ )['last_hidden_state']

												# select random slice
												UpperCAmelCase					=			ids_tensor((1,)       ,  output_from_past.shape[-1] ).item()
												UpperCAmelCase					=			output_from_no_past[:, -1, random_slice_idx].detach()
												UpperCAmelCase					=			output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(lowercase_       ,  lowercase_       ,  atol=1E-3 ) )






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:int       ,  lowercase_			:Tuple       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Dict       ,  lowercase_			:Any       ,  *lowercase_			:Tuple )    ->      int:
												UpperCAmelCase					=			BioGptModel(config=lowercase_ ).to(lowercase_ ).eval()

												UpperCAmelCase					=			torch.ones(input_ids.shape       ,  dtype=torch.long       ,  device=lowercase_ )

												# first forward pass
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  use_cache=lowercase_ )

												UpperCAmelCase				,   UpperCAmelCase					=			outputs.to_tuple()

												# create hypothetical multiple next token and extent to next_input_ids
												UpperCAmelCase					=			ids_tensor((self.batch_size, 3)       ,  config.vocab_size )
												UpperCAmelCase					=			ids_tensor((self.batch_size, 3)       ,  2 )

												# append to next input_ids and
												UpperCAmelCase					=			torch.cat([input_ids, next_tokens]       ,  dim=-1 )
												UpperCAmelCase					=			torch.cat([attention_mask, next_attn_mask]       ,  dim=-1 )

												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_ )['last_hidden_state']
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  past_key_values=lowercase_ )[
												    'last_hidden_state'
												]

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

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(lowercase_       ,  lowercase_       ,  atol=1E-3 ) )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Tuple       ,  lowercase_			:Tuple       ,  lowercase_			:List[str]       ,  lowercase_			:Dict       ,  lowercase_			:Union[str, Any]       ,  *lowercase_			:Optional[int]       ,  lowercase_			:Optional[Any]=False )    ->      Optional[int]:
												UpperCAmelCase					=			BioGptForCausalLM(lowercase_ )
												model.to(lowercase_ )
												if gradient_checkpointing:
																		model.gradient_checkpointing_enable()

												UpperCAmelCase					=			model(lowercase_       ,  labels=lowercase_ )
												self.parent.assertEqual(result.loss.shape       ,  () )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size) )
												result.loss.backward()






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:Union[str, Any]       ,  *lowercase_			:Optional[int] )    ->      Tuple:
												UpperCAmelCase					=			BioGptModel(lowercase_ )
												UpperCAmelCase					=			model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
												for key in model.state_dict().keys():
																		if "c_proj" in key and "weight" in key:
																								self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std )       ,  0.001 )
																								self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )       ,  0.01 )






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:str       ,  lowercase_			:Optional[Any]       ,  lowercase_			:str       ,  lowercase_			:Any       ,  lowercase_			:Tuple       ,  *lowercase_			:List[str] )    ->      List[Any]:
												UpperCAmelCase					=			self.num_labels
												UpperCAmelCase					=			BioGptForTokenClassification(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  token_type_ids=lowercase_ )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels) )






						def      UpperCAmelCase__					( self			:Any )    ->      int:
												UpperCAmelCase					=			self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   (
												    UpperCAmelCase
												)				,   
												)					=			config_and_inputs
												UpperCAmelCase					=			{'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						__UpperCamelCase		    =							(BioGptForCausalLM,) if is_torch_available() else ()
						__UpperCamelCase		    =							(
						    {
						        """feature-extraction""": BioGptModel,
						        """text-classification""": BioGptForSequenceClassification,
						        """text-generation""": BioGptForCausalLM,
						        """token-classification""": BioGptForTokenClassification,
						        """zero-shot""": BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCamelCase		    =							False






						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												UpperCAmelCase					=			BioGptModelTester(self )
												UpperCAmelCase					=			ConfigTester(self       ,  config_class=lowercase_       ,  hidden_size=37 )






						def      UpperCAmelCase__					( self			:Any )    ->      Optional[int]:
												self.config_tester.run_common_tests()






						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																		UpperCAmelCase					=			type
																		self.model_tester.create_and_check_model(*lowercase_ )






						def      UpperCAmelCase__					( self			:Dict )    ->      Dict:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase_ )






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Optional[int]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_forward_and_backwards(*lowercase_       ,  gradient_checkpointing=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple )    ->      List[str]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      str:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase_ )






						@slow
						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
												model.to(lowercase_ )
												UpperCAmelCase					=			BioGptTokenizer.from_pretrained('microsoft/biogpt' )

												UpperCAmelCase					=			'left'

												# Define PAD Token = EOS Token = 50256
												UpperCAmelCase					=			tokenizer.eos_token
												UpperCAmelCase					=			model.config.eos_token_id

												# use different length sentences to test batching
												UpperCAmelCase					=			[
												    'Hello, my dog is a little',
												    'Today, I',
												]

												UpperCAmelCase					=			tokenizer(lowercase_       ,  return_tensors='pt'       ,  padding=lowercase_ )
												UpperCAmelCase					=			inputs['input_ids'].to(lowercase_ )

												UpperCAmelCase					=			model.generate(
												    input_ids=lowercase_       ,  attention_mask=inputs['attention_mask'].to(lowercase_ )       ,  )

												UpperCAmelCase					=			tokenizer(sentences[0]       ,  return_tensors='pt' ).input_ids.to(lowercase_ )
												UpperCAmelCase					=			model.generate(input_ids=lowercase_ )

												UpperCAmelCase					=			inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
												UpperCAmelCase					=			tokenizer(sentences[1]       ,  return_tensors='pt' ).input_ids.to(lowercase_ )
												UpperCAmelCase					=			model.generate(input_ids=lowercase_       ,  max_length=model.config.max_length - num_paddings )

												UpperCAmelCase					=			tokenizer.batch_decode(lowercase_       ,  skip_special_tokens=lowercase_ )
												UpperCAmelCase					=			tokenizer.decode(output_non_padded[0]       ,  skip_special_tokens=lowercase_ )
												UpperCAmelCase					=			tokenizer.decode(output_padded[0]       ,  skip_special_tokens=lowercase_ )

												UpperCAmelCase					=			[
												    'Hello, my dog is a little bit bigger than a little bit.',
												    'Today, I have a good idea of how to use the information',
												]
												self.assertListEqual(lowercase_       ,  lowercase_ )
												self.assertListEqual(lowercase_       ,  [non_padded_sentence, padded_sentence] )






						@slow
						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase					=			BioGptModel.from_pretrained(lowercase_ )
																		self.assertIsNotNone(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase					=			3
												UpperCAmelCase					=			input_dict['input_ids']
												UpperCAmelCase					=			input_ids.ne(1 ).to(lowercase_ )
												UpperCAmelCase					=			ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size )
												UpperCAmelCase					=			BioGptForSequenceClassification(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  labels=lowercase_ )
												self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels) )






						def      UpperCAmelCase__					( self			:Dict )    ->      List[Any]:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase					=			3
												UpperCAmelCase					=			'multi_label_classification'
												UpperCAmelCase					=			input_dict['input_ids']
												UpperCAmelCase					=			input_ids.ne(1 ).to(lowercase_ )
												UpperCAmelCase					=			ids_tensor(
												    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size ).to(torch.float )
												UpperCAmelCase					=			BioGptForSequenceClassification(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  attention_mask=lowercase_       ,  labels=lowercase_ )
												self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels) )







@require_torch
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						@slow
						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												UpperCAmelCase					=			BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
												UpperCAmelCase					=			torch.tensor([[2, 48_05, 9, 6_56, 21]] )
												UpperCAmelCase					=			model(lowercase_ )[0]

												UpperCAmelCase					=			4_23_84

												UpperCAmelCase					=			torch.Size((1, 5, vocab_size) )
												self.assertEqual(output.shape       ,  lowercase_ )

												UpperCAmelCase					=			torch.tensor(
												    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )

												self.assertTrue(torch.allclose(output[:, :3, :3]       ,  lowercase_       ,  atol=1E-4 ) )






						@slow
						def      UpperCAmelCase__					( self			:List[str] )    ->      List[Any]:
												UpperCAmelCase					=			BioGptTokenizer.from_pretrained('microsoft/biogpt' )
												UpperCAmelCase					=			BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
												model.to(lowercase_ )

												torch.manual_seed(0 )
												UpperCAmelCase					=			tokenizer('COVID-19 is'       ,  return_tensors='pt' ).to(lowercase_ )
												UpperCAmelCase					=			model.generate(
												    **lowercase_       ,  min_length=1_00       ,  max_length=10_24       ,  num_beams=5       ,  early_stopping=lowercase_       ,  )
												UpperCAmelCase					=			tokenizer.decode(output_ids[0]       ,  skip_special_tokens=lowercase_ )

												UpperCAmelCase					=			(
												    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
												    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
												    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
												    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
												    ' more than 800,000 deaths.'
												)
												self.assertEqual(lowercase_       ,  lowercase_ )










"""simple docstring"""


import math

import flax.linen as nn
import jax.numpy as jnp

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_ = 1		,     lowercase_ = 1		,     lowercase_ = 1.0e4		,     lowercase_ = False		,     lowercase_ = 1.0		,     ):
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
						UpperCAmelCase					=			float(embedding_dim // 2						)
						UpperCAmelCase					=			math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						UpperCAmelCase					=			min_timescale * jnp.exp(jnp.arange(lowercase_		,     dtype=jnp.floataa						) * -log_timescale_increment						)
						UpperCAmelCase					=			jnp.expand_dims(lowercase_		,     1						) * jnp.expand_dims(lowercase_		,     0						)

						# scale embeddings
						UpperCAmelCase					=			scale * emb

						if flip_sin_to_cos:
												UpperCAmelCase					=			jnp.concatenate([jnp.cos(lowercase_						), jnp.sin(lowercase_						)]		,     axis=1						)
						else:
												UpperCAmelCase					=			jnp.concatenate([jnp.sin(lowercase_						), jnp.cos(lowercase_						)]		,     axis=1						)
						UpperCAmelCase					=			jnp.reshape(lowercase_		,     [jnp.shape(lowercase_						)[0], embedding_dim]						)
						return signal







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							jnp.floataa






						@nn.compact
						def __call__( self			:Union[str, Any]       ,  lowercase_			:Tuple )    ->      str:
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_1' )(lowercase_ )
												UpperCAmelCase					=			nn.silu(lowercase_ )
												UpperCAmelCase					=			nn.Dense(self.time_embed_dim       ,  dtype=self.dtype       ,  name='linear_2' )(lowercase_ )
												return temb







class 				A_      (					nn.Module    ):



						"""simple docstring"""
						__UpperCamelCase		    =							32
						__UpperCamelCase		    =							False
						__UpperCamelCase		    =							1






						@nn.compact
						def __call__( self			:Any       ,  lowercase_			:int )    ->      Union[str, Any]:
												return get_sinusoidal_embeddings(
												    lowercase_       ,  embedding_dim=self.dim       ,  flip_sin_to_cos=self.flip_sin_to_cos       ,  freq_shift=self.freq_shift )





