

from typing import List, Optional, Tuple, Union

import torch

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


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

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=lowercase      ,      scheduler=lowercase       )


			@torch.no_grad()
			def __call__(    self      ,      lowercase = 1      ,      lowercase = 100      ,      lowercase = None      ,      lowercase = None      ,      lowercase = True      ,      )  ->						Union[AudioPipelineOutput, Tuple]:
							'''simple docstring'''






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

							A__        =			audio_length_in_s * self.unet.config.sample_rate

							A__        =			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}.'       )

							A__        =			int(lowercase       )
							if sample_size % down_scale_factor != 0:
											A__        =			(
											    (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."       )
							A__        =			int(lowercase       )

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

							A__        =			randn_tensor(lowercase      ,      generator=lowercase      ,      device=self.device      ,      dtype=lowercase       )

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

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

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

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

							A__        =			audio[:, :, :original_sample_size]

							if not return_dict:
											return (audio,)

							return AudioPipelineOutput(audios=lowercase       )




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      dtype=tf.floataa      ,      )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,      expected_slice.numpy()      ,      atol=1e-4       )       )

