



"""simple docstring"""






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch

class        __SCREAMING_SNAKE_CASE	(      TensorFormatter[Mapping, 'torch.Tensor', Mapping]   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  str=None,  **lowerCamelCase							:  int     )->					Optional[Any]:
								super().__init__(features=lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=torch_tensor_kwargs
								import torch  # noqa import torch at initialization



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					int:
								import torch

								if isinstance(lowerCamelCase,  lowerCamelCase     ) and column:
											if all(
											    isinstance(lowerCamelCase,  torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
											    for x in column     ):
														return torch.stack(lowerCamelCase     )
								return column



					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  List[Any]     )->					List[str]:
								import torch

								if isinstance(lowerCamelCase,  (str, bytes, type(lowerCamelCase     ))     ):
											return value
								elif isinstance(lowerCamelCase,  (np.character, np.ndarray)     ) and np.issubdtype(value.dtype,  np.character     ):
											return value.tolist()

								lowerCamelCase__		:		Any			={}

								if isinstance(lowerCamelCase,  (np.number, np.ndarray)     ) and np.issubdtype(value.dtype,  np.integer     ):
											lowerCamelCase__		:		Optional[Any]			={'''dtype''': torch.intaa}
								elif isinstance(lowerCamelCase,  (np.number, np.ndarray)     ) and np.issubdtype(value.dtype,  np.floating     ):
											lowerCamelCase__		:		Optional[Any]			={'''dtype''': torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
											import PIL.Image

											if isinstance(lowerCamelCase,  PIL.Image.Image     ):
														lowerCamelCase__		:		str			=np.asarray(lowerCamelCase     )
								return torch.tensor(lowerCamelCase,  **{**default_dtype, **self.torch_tensor_kwargs}     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  Dict     )->					Optional[int]:
								import torch

								# support for torch, tf, jax etc.
								if hasattr(lowerCamelCase,  '''__array__'''     ) and not isinstance(lowerCamelCase,  torch.Tensor     ):
											lowerCamelCase__		:		List[str]			=data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(lowerCamelCase,  np.ndarray     ):
											if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
														return self._consolidate([self.recursive_tensorize(lowerCamelCase     ) for substruct in data_struct]     )
								elif isinstance(lowerCamelCase,  (list, tuple)     ):
											return self._consolidate([self.recursive_tensorize(lowerCamelCase     ) for substruct in data_struct]     )
								return self._tensorize(lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  dict     )->					Any:
								return map_nested(self._recursive_tensorize,  lowerCamelCase,  map_list=lowerCamelCase     )



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  pa.Table     )->					Mapping:
								lowerCamelCase__		:		List[Any]			=self.numpy_arrow_extractor().extract_row(lowerCamelCase     )
								lowerCamelCase__		:		Dict			=self.python_features_decoder.decode_row(lowerCamelCase     )
								return self.recursive_tensorize(lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  lowerCamelCase							:  pa.Table     )->					"torch.Tensor":
								lowerCamelCase__		:		Optional[Any]			=self.numpy_arrow_extractor().extract_column(lowerCamelCase     )
								lowerCamelCase__		:		Optional[Any]			=self.python_features_decoder.decode_column(lowerCamelCase,  pa_table.column_names[0]     )
								lowerCamelCase__		:		List[Any]			=self.recursive_tensorize(lowerCamelCase     )
								lowerCamelCase__		:		Dict			=self._consolidate(lowerCamelCase     )
								return column




					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  pa.Table     )->					Mapping:
								lowerCamelCase__		:		List[str]			=self.numpy_arrow_extractor().extract_batch(lowerCamelCase     )
								lowerCamelCase__		:		Any			=self.python_features_decoder.decode_batch(lowerCamelCase     )
								lowerCamelCase__		:		Optional[Any]			=self.recursive_tensorize(lowerCamelCase     )
								for column_name in batch:
											lowerCamelCase__		:		Union[str, Any]			=self._consolidate(batch[column_name]     )
								return batch









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




