





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


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

_a				:	Optional[int]           =       2_56




class 							UpperCamelCase_							(       _a		):







 """simple docstring"""

 A			 =       ["melgan"]


 def __init__(   self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				):
       super().__init__()

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

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


 def 		lowerCamelCase_ (   self  ,				UpperCAmelCase  ,				UpperCAmelCase=(-1.0, 1.0)  ,				UpperCAmelCase=False							):
       __lowerCamelCase   ,						__lowerCamelCase				   =       output_range
       if clip:
             __lowerCamelCase				   =       torch.clip(__lowerCamelCase  ,				self.min_value  ,				self.max_value							)
       # Scale to [0, 1].
       __lowerCamelCase				   =       (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  ,				UpperCAmelCase  ,				UpperCAmelCase=(-1.0, 1.0)  ,				UpperCAmelCase=False							):
       __lowerCamelCase   ,						__lowerCamelCase				   =       input_range
       __lowerCamelCase				   =       torch.clip(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase							) if clip else outputs
       # Scale to [0, 1].
       __lowerCamelCase				   =       (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  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase							):
       __lowerCamelCase				   =       input_tokens > 0
       __lowerCamelCase   ,						__lowerCamelCase				   =       self.notes_encoder(
           encoder_input_tokens=__lowerCamelCase  ,				encoder_inputs_mask=__lowerCamelCase							)

       __lowerCamelCase   ,						__lowerCamelCase				   =       self.continuous_encoder(
           encoder_inputs=__lowerCamelCase  ,				encoder_inputs_mask=__lowerCamelCase							)

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


 def 		lowerCamelCase_ (   self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase							):
       __lowerCamelCase				   =       noise_time
       if not torch.is_tensor(__lowerCamelCase							):
             __lowerCamelCase				   =       torch.tensor([timesteps]  ,				dtype=torch.long  ,				device=input_tokens.device							)
       elif torch.is_tensor(__lowerCamelCase							) and len(timesteps.shape							) == 0:
             __lowerCamelCase				   =       timesteps[None].to(input_tokens.device							)

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

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



 @torch.no_grad()
 def __call__(   self  ,				UpperCAmelCase  ,				UpperCAmelCase = None  ,				UpperCAmelCase = 1_0_0  ,				UpperCAmelCase = True  ,				UpperCAmelCase = "numpy"  ,				UpperCAmelCase = None  ,				UpperCAmelCase = 1  ,				):
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__lowerCamelCase  ,				__lowerCamelCase							) or callback_steps <= 0)
       ):
             raise ValueError(
                 f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                 f''' {type(__lowerCamelCase							)}.'''							)

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

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

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

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

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

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

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

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

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

             __lowerCamelCase				   =       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(__lowerCamelCase  ,				__lowerCamelCase							)

             logger.info("""Generated segment"""  ,				__lowerCamelCase							)

       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				   =       self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
             __lowerCamelCase				   =       full_pred_mel

       if not return_dict:
             return (output,)

       return AudioPipelineOutput(audios=__lowerCamelCase							)












from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__magic_name__  :  Any					=       TypeVar("""KEY""")
__magic_name__  :  Dict					=       TypeVar("""VAL""")





@dataclass(frozen=_a    ,					slots=_a			)
class     SCREAMING_SNAKE_CASE__		(Generic[KEY, VAL]			):
     lowercase_ :      KEY
     lowercase_ :      VAL





class     SCREAMING_SNAKE_CASE__		(_Item			):
     def __init__(						self	:		Tuple						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						__lowerCamelCase						)



     def __bool__(						self	:		Tuple						):



       """simple docstring"""
       return False



__magic_name__  :  str					=       _DeletedItem()





class     SCREAMING_SNAKE_CASE__		(MutableMapping[KEY, VAL]			):
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		int = 8       ,						__lowerCamelCase	:		float = 0.75						):



       """simple docstring"""
       lowerCAmelCase__										=      initial_block_size
       lowerCAmelCase__										=      [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCAmelCase__										=      capacity_factor
       lowerCAmelCase__										=      0
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       return hash(__lowerCamelCase						) % len(self._buckets						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       return (ind + 1) % len(self._buckets						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets[ind]
       if not stored:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         self._len += 1
         return True
       elif stored.key == key:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         return True
       else:
         return False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if len(self._buckets						) <= self._initial_block_size:
         return False
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets
       lowerCAmelCase__										=      [None] * new_size
       lowerCAmelCase__										=      0
       for item in old_buckets:
         if item:
           self._add_item(item.key       ,						item.val						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self._resize(len(self._buckets						) * 2						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self._resize(len(self._buckets						) // 2						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       lowerCAmelCase__										=      self._get_bucket_index(__lowerCamelCase						)
       for _ in range(len(self._buckets						)						):
         yield ind
         lowerCAmelCase__										=      self._get_next_ind(__lowerCamelCase						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         if self._try_set(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						):
           break
     def __setitem__(						self	:		str       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       if self._is_full():
         self._size_up()

       self._add_item(__lowerCamelCase       ,						__lowerCamelCase						)
     def __delitem__(						self	:		Tuple       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           raise KeyError(__lowerCamelCase						)
         if item is _deleted:
           continue
         if item.key == key:
           lowerCAmelCase__										=      _deleted
           self._len -= 1
           break
       if self._is_sparse():
         self._size_down()
     def __getitem__(						self	:		Optional[int]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           break
         if item is _deleted:
           continue
         if item.key == key:
           return item.val
       raise KeyError(__lowerCamelCase						)
     def __len__(						self	:		int						):



       """simple docstring"""
       return self._len
     def __iter__(						self	:		Union[str, Any]						):



       """simple docstring"""
       yield from (item.key for item in self._buckets if item)



     def __repr__(						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''' ,'''.join(
           F"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return F"""HashMap({val_string})"""

