

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
	from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


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

lowerCAmelCase__	   = 2_5_6

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







			__lowerCamelCase        =       ['melgan']


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						None:
							'''simple docstring'''






							super().__init__()

							# From MELGAN
							A__        =			math.log(1e-5       )  # Matches MelGAN training.
							A__        =			4.0  # Largest value for most examples
							A__        =			128

							self.register_modules(
							    notes_encoder=lowercase      ,      continuous_encoder=lowercase      ,      decoder=lowercase      ,      scheduler=lowercase      ,      melgan=lowercase      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=(-1.0, 1.0)      ,      lowercase=False       )  ->						str:
							'''simple docstring'''






							A__    ,				A__        =			output_range
							if clip:
											A__        =			torch.clip(lowercase      ,      self.min_value      ,      self.max_value       )
							# Scale to [0, 1].
							A__        =			(features - self.min_value) / (self.max_value - self.min_value)
							# Scale to [min_out, max_out].
							return zero_one * (max_out - min_out) + min_out


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=(-1.0, 1.0)      ,      lowercase=False       )  ->						Optional[int]:
							'''simple docstring'''






							A__    ,				A__        =			input_range
							A__        =			torch.clip(lowercase      ,      lowercase      ,      lowercase       ) if clip else outputs
							# Scale to [0, 1].
							A__        =			(outputs - min_out) / (max_out - min_out)
							# Scale to [self.min_value, self.max_value].
							return zero_one * (self.max_value - self.min_value) + self.min_value


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							A__        =			input_tokens > 0
							A__    ,				A__        =			self.notes_encoder(
							    encoder_input_tokens=lowercase      ,      encoder_inputs_mask=lowercase       )

							A__    ,				A__        =			self.continuous_encoder(
							    encoder_inputs=lowercase      ,      encoder_inputs_mask=lowercase       )

							return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			noise_time
							if not torch.is_tensor(lowercase       ):
											A__        =			torch.tensor([timesteps]      ,      dtype=torch.long      ,      device=input_tokens.device       )
							elif torch.is_tensor(lowercase       ) and len(timesteps.shape       ) == 0:
											A__        =			timesteps[None].to(input_tokens.device       )

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							A__        =			timesteps * torch.ones(input_tokens.shape[0]      ,      dtype=timesteps.dtype      ,      device=timesteps.device       )

							A__        =			self.decoder(
							    encodings_and_masks=lowercase      ,      decoder_input_tokens=lowercase      ,      decoder_noise_time=lowercase       )
							return logits


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






							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(lowercase      ,      lowercase       ) or callback_steps <= 0)
							):
											raise ValueError(
											    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
											    F' {type(lowercase       )}.'       )

							A__        =			np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,      dtype=np.floataa       )
							A__        =			np.zeros([1, 0, self.n_dims]      ,      np.floataa       )
							A__        =			torch.ones((1, TARGET_FEATURE_LENGTH)      ,      dtype=lowercase      ,      device=self.device       )

							for i, encoder_input_tokens in enumerate(lowercase       ):
											if i == 0:
															A__        =			torch.from_numpy(pred_mel[:1].copy()       ).to(
															    device=self.device      ,      dtype=self.decoder.dtype       )
															# The first chunk has no previous context.
															A__        =			torch.zeros((1, TARGET_FEATURE_LENGTH)      ,      dtype=lowercase      ,      device=self.device       )
											else:
															# The full song pipeline does not feed in a context feature, so the mask
															# will be all 0s after the feature converter. Because we know we're
															# feeding in a full context chunk from the previous prediction, set it
															# to all 1s.
															A__        =			ones

											A__        =			self.scale_features(
											    lowercase      ,      output_range=[-1.0, 1.0]      ,      clip=lowercase       )

											A__        =			self.encode(
											    input_tokens=torch.IntTensor([encoder_input_tokens]       ).to(device=self.device       )      ,      continuous_inputs=lowercase      ,      continuous_mask=lowercase      ,      )

											# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
											A__        =			randn_tensor(
											    shape=encoder_continuous_inputs.shape      ,      generator=lowercase      ,      device=self.device      ,      dtype=self.decoder.dtype      ,      )

											# set step values
											self.scheduler.set_timesteps(lowercase       )

											# Denoising diffusion loop
											for j, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
															A__        =			self.decode(
															    encodings_and_masks=lowercase      ,      input_tokens=lowercase      ,      noise_time=t / self.scheduler.config.num_train_timesteps      ,      )

															# Compute previous output: x_t -> x_t-1
															A__        =			self.scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      generator=lowercase       ).prev_sample

											A__        =			self.scale_to_features(lowercase      ,      input_range=[-1.0, 1.0]       )
											A__        =			mel[:1]
											A__        =			mel.cpu().float().numpy()

											A__        =			np.concatenate([full_pred_mel, pred_mel[:1]]      ,      axis=1       )

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
															callback(lowercase      ,      lowercase       )

											logger.info("Generated segment"      ,      lowercase       )

							if output_type == "numpy" and not is_onnx_available():
											raise ValueError(
											    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."       )
							elif output_type == "numpy" and self.melgan is None:
											raise ValueError(
											    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."       )

							if output_type == "numpy":
											A__        =			self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
							else:
											A__        =			full_pred_mel

							if not return_dict:
											return (output,)

							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       )       )

