






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowercase  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a					)       ->    List[Any]:
								super().__init__()
								self.register_modules(unet=a       ,			scheduler=a					)






	@torch.no_grad()
	def __call__(       self       ,			a = 1       ,			a = 1_00       ,			a = None       ,			a = None       ,			a = True       ,			)       ->    Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
															snake_case_				=							self.unet.config.sample_size / self.unet.config.sample_rate

								snake_case_				=							audio_length_in_s * self.unet.config.sample_rate

								snake_case_				=							2 ** len(self.unet.up_blocks					)
								if sample_size < 3 * down_scale_factor:
															raise ValueError(
															    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
															    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''					)

								snake_case_				=							int(a					)
								if sample_size % down_scale_factor != 0:
															snake_case_				=							(
															    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
															) * down_scale_factor
															logger.info(
															    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
															    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
															    ' process.'					)
								snake_case_				=							int(a					)

								snake_case_				=							next(iter(self.unet.parameters()					)					).dtype
								snake_case_				=							(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(a       ,			a					) and len(a					) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a					)}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

								snake_case_				=							randn_tensor(a       ,			generator=a       ,			device=self.device       ,			dtype=a					)

								# set step values
								self.scheduler.set_timesteps(a       ,			device=audio.device					)
								snake_case_				=							self.scheduler.timesteps.to(a					)

								for t in self.progress_bar(self.scheduler.timesteps					):
															# 1. predict noise model_output
															snake_case_				=							self.unet(a       ,			a					).sample

															# 2. compute previous image: x_t -> t_t-1
															snake_case_				=							self.scheduler.step(a       ,			a       ,			a					).prev_sample

								snake_case_				=							audio.clamp(-1       ,			1					).float().cpu().numpy()

								snake_case_				=							audio[:, :, :original_sample_size]

								if not return_dict:
															return (audio,)

								return AudioPipelineOutput(audios=a					)













import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								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_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								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_				=							RobertaConfig(
								    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=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			vocab_size=2					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





