

from typing import List, Optional, Tuple, Union

import torch

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


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






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__()
								self.register_modules(unet=SCREAMING_SNAKE_CASE__       ,scheduler=SCREAMING_SNAKE_CASE__)


							@torch.no_grad()
							def __call__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      int = 1_0_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[float] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,):

								if audio_length_in_s is None:
									__lowerCamelCase    :    List[str] 	=			self.unet.config.sample_size / self.unet.config.sample_rate

								__lowerCamelCase    :    Any 	=			audio_length_in_s * self.unet.config.sample_rate

								__lowerCamelCase    :    Optional[int] 	=			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}.")

								__lowerCamelCase    :    Any 	=			int(SCREAMING_SNAKE_CASE__)
								if sample_size % down_scale_factor != 0:
									__lowerCamelCase    :    Tuple 	=			(
									    (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.')
								__lowerCamelCase    :    Union[str, Any] 	=			int(SCREAMING_SNAKE_CASE__)

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

								__lowerCamelCase    :    Optional[Any] 	=			randn_tensor(SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,device=self.device       ,dtype=SCREAMING_SNAKE_CASE__)

								# set step values
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       ,device=audio.device)
								__lowerCamelCase    :    int 	=			self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE__)

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

									# 2. compute previous image: x_t -> t_t-1
									__lowerCamelCase    :    Union[str, Any] 	=			self.scheduler.step(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__).prev_sample

								__lowerCamelCase    :    Any 	=			audio.clamp(-1       ,1).float().cpu().numpy()

								__lowerCamelCase    :    str 	=			audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE__)







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    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			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))




