



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: List[str]    = 'markuplm'



 def __init__(self				,     A=30_522				,     A=768				,     A=12				,     A=12				,     A=3_072				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=512				,     A=2				,     A=0.02				,     A=1E-12				,     A=0				,     A=0				,     A=2				,     A=256				,     A=1_024				,     A=216				,     A=1_001				,     A=32				,     A=50				,     A="absolute"				,     A=True				,     A=None				,     **A				,     )					->  Optional[int]:


      """simple docstring"""



      super().__init__(
          pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )
      _a											=						vocab_size
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						hidden_act
      _a											=						intermediate_size
      _a											=						hidden_dropout_prob
      _a											=						attention_probs_dropout_prob
      _a											=						max_position_embeddings
      _a											=						type_vocab_size
      _a											=						initializer_range
      _a											=						layer_norm_eps
      _a											=						position_embedding_type
      _a											=						use_cache
      _a											=						classifier_dropout
      # additional properties
      _a											=						max_depth
      _a											=						max_xpath_tag_unit_embeddings
      _a											=						max_xpath_subs_unit_embeddings
      _a											=						tag_pad_id
      _a											=						subs_pad_id
      _a											=						xpath_unit_hidden_size








'''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


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

lowercase_	 =						256






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = ['melgan']



 def __init__(self				,     A				,     A				,     A				,     A				,     A				,     )					->  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=A				,     continuous_encoder=A				,     decoder=A				,     scheduler=A				,     melgan=A				,     )



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  List[str]:


      """simple docstring"""



      _a						,					_a											=						output_range
      if clip:
           _a											=						torch.clip(A				,     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       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  Optional[Any]:


      """simple docstring"""



      _a						,					_a											=						input_range
      _a											=						torch.clip(A				,     A				,     A ) 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       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						input_tokens > 0
      _a						,					_a											=						self.notes_encoder(
          encoder_input_tokens=A				,     encoder_inputs_mask=A )

      _a						,					_a											=						self.continuous_encoder(
          encoder_inputs=A				,     encoder_inputs_mask=A )

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



 def       a__						(self				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						noise_time
      if not torch.is_tensor(A ):
           _a											=						torch.tensor([timesteps]				,     dtype=torch.long				,     device=input_tokens.device )
      elif torch.is_tensor(A ) 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=A				,     decoder_input_tokens=A				,     decoder_noise_time=A )
      return logits



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


      """simple docstring"""



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

      _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=A				,     device=self.device )

      for i, encoder_input_tokens in enumerate(A ):
           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=A				,     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(
               A				,     output_range=[-1.0, 1.0]				,     clip=A )

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

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

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

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

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

           _a											=						self.scale_to_features(A				,     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(A				,     A )

           logger.info('''Generated segment'''				,     A )

      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=A )



