'''simple docstring'''





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class 	__A				(			UpperCamelCase__     ):
				a__	:						Union[str, Any]	       =  ["""vqvae"""]



				def __init__(self   :   Optional[Any]       ,      __a   :   Optional[int]       ,      __a   :   Optional[int]       ,      __a   :   Optional[int]       ,      __a   :   Any       ,      ):
									super().__init__()
									self.register_modules(unet=_snake_case       ,      scheduler=_snake_case       ,      mel=_snake_case       ,      vqvae=_snake_case			)



				def  _lowercase						(self   :   Optional[Any]			):
									return 50 if isinstance(self.scheduler       ,      _snake_case			) else 1000



				@torch.no_grad()
				def __call__(self   :   List[Any]       ,      __a   :   str = 1       ,      __a   :   Optional[int] = None       ,      __a   :   Tuple = None       ,      __a   :   Union[str, Any] = 0       ,      __a   :   Tuple = 0       ,      __a   :   Any = None       ,      __a   :   Union[str, Any] = None       ,      __a   :   int = 0       ,      __a   :   int = 0       ,      __a   :   List[str] = None       ,      __a   :   str = 0       ,      __a   :   int = None       ,      __a   :   Optional[Any] = None       ,      __a   :   Tuple=True       ,      ):
									UpperCAmelCase_	   =       steps or self.get_default_steps()
									self.scheduler.set_timesteps(_snake_case			)
									UpperCAmelCase_	   =       step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size			) == int:
														UpperCAmelCase_	   =       (self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
														UpperCAmelCase_	   =       randn_tensor(
														    (
														        batch_size,
														        self.unet.config.in_channels,
														        self.unet.config.sample_size[0],
														        self.unet.config.sample_size[1],
														    )       ,      generator=_snake_case       ,      device=self.device       ,      )
									UpperCAmelCase_	   =       noise
									UpperCAmelCase_	   =       None

									if audio_file is not None or raw_audio is not None:
														self.mel.load_audio(_snake_case       ,      _snake_case			)
														UpperCAmelCase_	   =       self.mel.audio_slice_to_image(_snake_case			)
														UpperCAmelCase_	   =       np.frombuffer(input_image.tobytes()       ,      dtype="uint8"			).reshape(
														    (input_image.height, input_image.width)			)
														UpperCAmelCase_	   =       (input_image / 255) * 2 - 1
														UpperCAmelCase_	   =       torch.tensor(input_image[np.newaxis, :, :]       ,      dtype=torch.float			).to(self.device			)

														if self.vqvae is not None:
																			UpperCAmelCase_	   =       self.vqvae.encode(torch.unsqueeze(_snake_case       ,      0			)			).latent_dist.sample(
																			    generator=_snake_case			)[0]
																			UpperCAmelCase_	   =       self.vqvae.config.scaling_factor * input_images

														if start_step > 0:
																			UpperCAmelCase_	   =       self.scheduler.add_noise(_snake_case       ,      _snake_case       ,      self.scheduler.timesteps[start_step - 1]			)

														UpperCAmelCase_	   =       (
														    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
														)
														UpperCAmelCase_	   =       int(mask_start_secs * pixels_per_second			)
														UpperCAmelCase_	   =       int(mask_end_secs * pixels_per_second			)
														UpperCAmelCase_	   =       self.scheduler.add_noise(_snake_case       ,      _snake_case       ,      torch.tensor(self.scheduler.timesteps[start_step:]			)			)

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]			)			):
														if isinstance(self.unet       ,      _snake_case			):
																			UpperCAmelCase_	   =       self.unet(_snake_case       ,      _snake_case       ,      _snake_case			)["sample"]
														else:
																			UpperCAmelCase_	   =       self.unet(_snake_case       ,      _snake_case			)["sample"]

														if isinstance(self.scheduler       ,      _snake_case			):
																			UpperCAmelCase_	   =       self.scheduler.step(
																			    model_output=_snake_case       ,      timestep=_snake_case       ,      sample=_snake_case       ,      eta=_snake_case       ,      generator=_snake_case       ,      )["prev_sample"]
														else:
																			UpperCAmelCase_	   =       self.scheduler.step(
																			    model_output=_snake_case       ,      timestep=_snake_case       ,      sample=_snake_case       ,      generator=_snake_case       ,      )["prev_sample"]

														if mask is not None:
																			if mask_start > 0:
																								UpperCAmelCase_	   =       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																								UpperCAmelCase_	   =       mask[:, step, :, -mask_end:]

									if self.vqvae is not None:
														# 0.18215 was scaling factor used in training to ensure unit variance
														UpperCAmelCase_	   =       1 / self.vqvae.config.scaling_factor * images
														UpperCAmelCase_	   =       self.vqvae.decode(_snake_case			)["sample"]

									UpperCAmelCase_	   =       (images / 2 + 0.5).clamp(0       ,      1			)
									UpperCAmelCase_	   =       images.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
									UpperCAmelCase_	   =       (images * 255).round().astype("uint8"			)
									UpperCAmelCase_	   =       list(
									    (Image.fromarray(_[:, :, 0]			) for _ in images)
									    if images.shape[3] == 1
									    else (Image.fromarray(_snake_case       ,      mode="RGB"			).convert("L"			) for _ in images)			)

									UpperCAmelCase_	   =       [self.mel.image_to_audio(_snake_case			) for _ in images]
									if not return_dict:
														return images, (self.mel.get_sample_rate(), audios)

									return BaseOutput(**AudioPipelineOutput(np.array(_snake_case			)[:, np.newaxis, :]			)       ,      **ImagePipelineOutput(_snake_case			)			)



				@torch.no_grad()
				def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   Any = 50			):
									assert isinstance(self.scheduler       ,      _snake_case			)
									self.scheduler.set_timesteps(_snake_case			)
									UpperCAmelCase_	   =       np.array(
									    [np.frombuffer(image.tobytes()       ,      dtype="uint8"			).reshape((1, image.height, image.width)			) for image in images]			)
									UpperCAmelCase_	   =       (sample / 255) * 2 - 1
									UpperCAmelCase_	   =       torch.Tensor(_snake_case			).to(self.device			)

									for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,      (0,)			)			):
														UpperCAmelCase_	   =       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
														UpperCAmelCase_	   =       self.scheduler.alphas_cumprod[t]
														UpperCAmelCase_	   =       (
														    self.scheduler.alphas_cumprod[prev_timestep]
														    if prev_timestep >= 0
														    else self.scheduler.final_alpha_cumprod
														)
														UpperCAmelCase_	   =       1 - alpha_prod_t
														UpperCAmelCase_	   =       self.unet(_snake_case       ,      _snake_case			)["sample"]
														UpperCAmelCase_	   =       (1 - alpha_prod_t_prev) ** 0.5 * model_output
														UpperCAmelCase_	   =       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
														UpperCAmelCase_	   =       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

									return sample






				@staticmethod
				def  _lowercase						(__a   :   Any       ,      __a   :   Optional[int]       ,      __a   :   Tuple			):
									UpperCAmelCase_	   =       acos(torch.dot(torch.flatten(_snake_case			)       ,      torch.flatten(_snake_case			)			) / torch.norm(_snake_case			) / torch.norm(_snake_case			)			)
									return sin((1 - alpha) * theta			) * xa / sin(_snake_case			) + sin(alpha * theta			) * xa / sin(_snake_case			)





'''simple docstring'''





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  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

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

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      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			)			)




