
"""simple docstring"""





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_a		       =							False
try:
    _a		       =							_is_package_available('google.colab')
except ModuleNotFoundError:
    pass


@input.register
class A_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_ = None				,   lowercase_ = []  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    0
     UpperCAmelCase_					:					Optional[Any]			     =    choices
     UpperCAmelCase_					:					List[Any]			     =    prompt
     if sys.platform == "win32":
        UpperCAmelCase_					:					Optional[int]			     =    "*"
     else:
        UpperCAmelCase_					:					int			     =    "➔ "







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = ""  ):

     """simple docstring"""
     if sys.platform != "win32":
        writeColor(self.choices[index]				,   32				,   lowercase_  )
     else:
        forceWrite(self.choices[index]				,   lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     if index == self.position:
        forceWrite(F""" {self.arrow_char} """  )
        self.write_choice(lowercase_  )
     else:
        forceWrite(F"""    {self.choices[index]}"""  )
     reset_cursor()







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = 1  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.position
     if direction == Direction.DOWN:
        if self.position + 1 >= len(self.choices  ):
           return
        self.position += num_spaces
     else:
        if self.position - 1 < 0:
           return
        self.position -= num_spaces
     clear_line()
     self.print_choice(lowercase_  )
     move_cursor(lowercase_				,   direction.name  )
     self.print_choice(self.position  )







  @input.mark(KEYMAP["up"]  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.move_direction(Direction.UP  )







  @input.mark(KEYMAP["down"]  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.move_direction(Direction.DOWN  )







  @input.mark(KEYMAP["newline"]  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     move_cursor(len(self.choices  ) - self.position				,   "DOWN"  )
     return self.position







  @input.mark(KEYMAP["interrupt"]  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     move_cursor(len(self.choices  ) - self.position				,   "DOWN"  )
     raise KeyboardInterrupt







  @input.mark_multiple(*[KEYMAP[str(lowercase_  )] for number in range(10  )]  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    int(chr(self.current_selection  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    index - self.position
     if index == self.position:
        return
     if index < len(self.choices  ):
        if self.position > index:
           self.move_direction(Direction.UP				,   -movement  )
        elif self.position < index:
           self.move_direction(Direction.DOWN				,   lowercase_  )
        else:
           return
     else:
        return







  def        UpperCamelCase__					(		self				,   lowercase_ = 0  ):

     """simple docstring"""
     if self.prompt:
        linebreak()
        forceWrite(self.prompt				,   "\n"  )
        if in_colab:
           forceWrite("Please input a choice index (starting from 0), and press enter"				,   "\n"  )
        else:
           forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"				,   "\n"  )
     UpperCAmelCase_					:					Optional[Any]			     =    default_choice
     for i in range(len(self.choices  )  ):
        self.print_choice(lowercase_  )
        forceWrite("\n"  )
     move_cursor(len(self.choices  ) - self.position				,   "UP"  )
     with cursor.hide():
        while True:
           if in_colab:
              try:
                 UpperCAmelCase_					:					Tuple			     =    int(builtins.input()  )
              except ValueError:
                 UpperCAmelCase_					:					List[Any]			     =    default_choice
           else:
              UpperCAmelCase_					:					Optional[int]			     =    self.handle_input()
           if choice is not None:
              reset_cursor()
              for _ in range(len(self.choices  ) + 1  ):
                 move_cursor(1				,   "UP"  )
                 clear_line()
              self.write_choice(lowercase_				,   "\n"  )
              return choice







"""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     )




