



"""simple docstring"""






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


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

__snake_case				:  Dict =       256



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['melgan']

	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						SpectrogramNotesEncoder			,     _SCREAMING_SNAKE_CASE:						SpectrogramContEncoder			,     _SCREAMING_SNAKE_CASE:						TaFilmDecoder			,     _SCREAMING_SNAKE_CASE:						DDPMScheduler			,     _SCREAMING_SNAKE_CASE:						OnnxRuntimeModel if is_onnx_available() else Any			,     )							->			None:

				"""simple docstring"""

				super().__init__()

				# From MELGAN
				__lowerCAmelCase :    List[str]				=			math.log(1e-5)  # Matches MelGAN training.
				__lowerCAmelCase :    List[str]				=			4.0  # Largest value for most examples
				__lowerCAmelCase :    Dict				=			128

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int=(-1.0, 1.0)			,     _SCREAMING_SNAKE_CASE:						List[str]=False)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			output_range
				if clip:
							__lowerCAmelCase :    Optional[Any]				=			torch.clip(_SCREAMING_SNAKE_CASE			,     self.min_value			,     self.max_value)
				# Scale to [0, 1].
				__lowerCAmelCase :    Dict				=			(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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Any=(-1.0, 1.0)			,     _SCREAMING_SNAKE_CASE:						Tuple=False)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Dict				=			input_range
				__lowerCAmelCase :    Optional[int]				=			torch.clip(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) if clip else outputs
				# Scale to [0, 1].
				__lowerCAmelCase :    Dict				=			(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 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			input_tokens > 0
				__lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			self.notes_encoder(
				    encoder_input_tokens=_SCREAMING_SNAKE_CASE			,     encoder_inputs_mask=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    Union[str, Any]				=			self.continuous_encoder(
				    encoder_inputs=_SCREAMING_SNAKE_CASE			,     encoder_inputs_mask=_SCREAMING_SNAKE_CASE)

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			noise_time
				if not torch.is_tensor(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Tuple				=			torch.tensor([timesteps]			,     dtype=torch.long			,     device=input_tokens.device)
				elif torch.is_tensor(_SCREAMING_SNAKE_CASE) and len(timesteps.shape) == 0:
							__lowerCAmelCase :    Optional[int]				=			timesteps[None].to(input_tokens.device)

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

				__lowerCAmelCase :    List[Any]				=			self.decoder(
				    encodings_and_masks=_SCREAMING_SNAKE_CASE			,     decoder_input_tokens=_SCREAMING_SNAKE_CASE			,     decoder_noise_time=_SCREAMING_SNAKE_CASE)
				return logits







	@torch.no_grad()
	def __call__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[List[int]]			,     _SCREAMING_SNAKE_CASE:						Optional[torch.Generator] = None			,     _SCREAMING_SNAKE_CASE:						int = 100			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						str = "numpy"			,     _SCREAMING_SNAKE_CASE:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,     _SCREAMING_SNAKE_CASE:						int = 1			,     )							->			Union[AudioPipelineOutput, Tuple]:

				"""simple docstring"""

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

				__lowerCAmelCase :    Union[str, Any]				=			np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			,     dtype=np.floataa)
				__lowerCAmelCase :    Optional[Any]				=			np.zeros([1, 0, self.n_dims]			,     np.floataa)
				__lowerCAmelCase :    Optional[int]				=			torch.ones((1, TARGET_FEATURE_LENGTH)			,     dtype=_SCREAMING_SNAKE_CASE			,     device=self.device)

				for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE):
							if i == 0:
										__lowerCAmelCase :    int				=			torch.from_numpy(pred_mel[:1].copy()).to(
										    device=self.device			,     dtype=self.decoder.dtype)
										# The first chunk has no previous context.
										__lowerCAmelCase :    Optional[int]				=			torch.zeros((1, TARGET_FEATURE_LENGTH)			,     dtype=_SCREAMING_SNAKE_CASE			,     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.
										__lowerCAmelCase :    Optional[int]				=			ones

							__lowerCAmelCase :    str				=			self.scale_features(
							    _SCREAMING_SNAKE_CASE			,     output_range=[-1.0, 1.0]			,     clip=_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase :    Dict				=			self.encode(
							    input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)			,     continuous_inputs=_SCREAMING_SNAKE_CASE			,     continuous_mask=_SCREAMING_SNAKE_CASE			,     )

							# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
							__lowerCAmelCase :    List[Any]				=			randn_tensor(
							    shape=encoder_continuous_inputs.shape			,     generator=_SCREAMING_SNAKE_CASE			,     device=self.device			,     dtype=self.decoder.dtype			,     )

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

							# Denoising diffusion loop
							for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
										__lowerCAmelCase :    Optional[int]				=			self.decode(
										    encodings_and_masks=_SCREAMING_SNAKE_CASE			,     input_tokens=_SCREAMING_SNAKE_CASE			,     noise_time=t / self.scheduler.config.num_train_timesteps			,     )

										# Compute previous output: x_t -> x_t-1
										__lowerCAmelCase :    Dict				=			self.scheduler.step(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     generator=_SCREAMING_SNAKE_CASE).prev_sample

							__lowerCAmelCase :    List[str]				=			self.scale_to_features(_SCREAMING_SNAKE_CASE			,     input_range=[-1.0, 1.0])
							__lowerCAmelCase :    Dict				=			mel[:1]
							__lowerCAmelCase :    Tuple				=			mel.cpu().float().numpy()

							__lowerCAmelCase :    Dict				=			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(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

							logger.info("Generated segment"			,     _SCREAMING_SNAKE_CASE)

				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":
							__lowerCAmelCase :    int				=			self.melgan(input_features=full_pred_mel.astype(np.floataa))
				else:
							__lowerCAmelCase :    str				=			full_pred_mel

				if not return_dict:
							return (output,)

				return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]