





"""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


lowerCamelCase_     :						Union[str, Any]						=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_     :						Dict						=					2_56

class  _UpperCAmelCase  (   lowerCAmelCase_		):



      '''simple docstring'''
      lowercase_      : Optional[Any]	    =   ["""melgan"""]

      def __init__(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        super().__init__()

        # From MELGAN
        A_  :       Any										=  math.log(1E-5  )  # Matches MelGAN training.
        A_  :       str										=  4.0  # Largest value for most examples
        A_  :       Any										=  1_2_8

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=(-1.0, 1.0)					,      snake_case_=False  ):

        """simple docstring"""

        A_						,       A_  :       List[str]										=  output_range
        if clip:
          A_  :       Tuple										=  torch.clip(__snake_case					,      self.min_value					,      self.max_value  )
        # Scale to [0, 1].
        A_  :       Optional[Any]										=  (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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=(-1.0, 1.0)					,      snake_case_=False  ):

        """simple docstring"""

        A_						,       A_  :       Union[str, Any]										=  input_range
        A_  :       Optional[int]										=  torch.clip(__snake_case					,      __snake_case					,      __snake_case  ) if clip else outputs
        # Scale to [0, 1].
        A_  :       List[str]										=  (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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       str										=  input_tokens > 0
        A_						,       A_  :       Tuple										=  self.notes_encoder(
            encoder_input_tokens=__snake_case					,      encoder_inputs_mask=__snake_case  )

        A_						,       A_  :       Optional[int]										=  self.continuous_encoder(
            encoder_inputs=__snake_case					,      encoder_inputs_mask=__snake_case  )

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  noise_time
        if not torch.is_tensor(__snake_case  ):
          A_  :       Any										=  torch.tensor([timesteps]					,      dtype=torch.long					,      device=input_tokens.device  )
        elif torch.is_tensor(__snake_case  ) and len(timesteps.shape  ) == 0:
          A_  :       List[Any]										=  timesteps[None].to(input_tokens.device  )

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

        A_  :       List[str]										=  self.decoder(
            encodings_and_masks=__snake_case					,      decoder_input_tokens=__snake_case					,      decoder_noise_time=__snake_case  )
        return logits

      @torch.no_grad()
      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = 1_0_0					,      snake_case_ = True					,      snake_case_ = "numpy"					,      snake_case_ = None					,      snake_case_ = 1					,      ):

        """simple docstring"""

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__snake_case					,      __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(__snake_case  )}."""  )

        A_  :       Dict										=  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,      dtype=np.floataa  )
        A_  :       List[str]										=  np.zeros([1, 0, self.n_dims]					,      np.floataa  )
        A_  :       int										=  torch.ones((1, TARGET_FEATURE_LENGTH)					,      dtype=__snake_case					,      device=self.device  )

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

          A_  :       str										=  self.scale_features(
              __snake_case					,      output_range=[-1.0, 1.0]					,      clip=__snake_case  )

          A_  :       List[Any]										=  self.encode(
              input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )					,      continuous_inputs=__snake_case					,      continuous_mask=__snake_case					,      )

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

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

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

            # Compute previous output: x_t -> x_t-1
            A_  :       int										=  self.scheduler.step(__snake_case					,      __snake_case					,      __snake_case					,      generator=__snake_case  ).prev_sample

          A_  :       str										=  self.scale_to_features(__snake_case					,      input_range=[-1.0, 1.0]  )
          A_  :       str										=  mel[:1]
          A_  :       Tuple										=  mel.cpu().float().numpy()

          A_  :       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(__snake_case					,      __snake_case  )

          logger.info('Generated segment'					,      __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":
          A_  :       int										=  self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
        else:
          A_  :       Dict										=  full_pred_mel

        if not return_dict:
          return (output,)

        return AudioPipelineOutput(audios=__snake_case  )


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =self.tokenizer(
						    text=__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					,  max_length=__snake_case					,  stride=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  return_overflowing_tokens=__snake_case					,  return_special_tokens_mask=__snake_case					,  return_offsets_mapping=__snake_case					,  return_length=__snake_case					,  verbose=__snake_case					,  return_tensors=__snake_case					,  **__snake_case					,  )
						# add pixel_values + pixel_mask
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






