



import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE           =  False, False, False

@dataclass
class 	lowerCamelCase_    :






						'''simple docstring'''
						a__   =  None
						a__   =  True
						a__   =  True
						a__   =  None
						# Automatically constructed
						a__   =  "dict"
						a__   =  pa.struct({"bytes": pa.binary(), "path": pa.string()}						)
						a__   =  field(default="Audio"						,init=_A						,repr=_A						)







						def __call__(	self      :			Optional[int] )      ->				Optional[int]:
									return self.pa_type







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, bytes, dict] )      ->				dict:
									try:
												import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
									except ImportError as err:
												raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												return {"bytes": None, "path": value}
									elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												return {"bytes": value, "path": None}
									elif "array" in value:
												# convert the audio array to wav bytes
												A							:					Union[str, Any]              =						BytesIO()
												sf.write(__lowerCamelCase	,	value["array"]	,	value["sampling_rate"]	,	format="wav" )
												return {"bytes": buffer.getvalue(), "path": None}
									elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
												# we set "bytes": None to not duplicate the data if they're already available locally
												if value["path"].endswith("pcm" ):
															# "PCM" only has raw audio bytes
															if value.get("sampling_rate" ) is None:
																		# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																		raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
															if value.get("bytes" ):
																		# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																		A							:					Tuple              =						np.frombuffer(value["bytes"]	,	dtype=np.intaa ).astype(np.floataa ) / 3_27_67
															else:
																		A							:					Dict              =						np.memmap(value["path"]	,	dtype="h"	,	mode="r" ).astype(np.floataa ) / 3_27_67

															A							:					Optional[int]              =						BytesIO(bytes() )
															sf.write(__lowerCamelCase	,	__lowerCamelCase	,	value["sampling_rate"]	,	format="wav" )
															return {"bytes": buffer.getvalue(), "path": None}
												else:
															return {"bytes": None, "path": value.get("path" )}
									elif value.get("bytes" ) is not None or value.get("path" ) is not None:
												# store the audio bytes, and path is used to infer the audio format using the file extension
												return {"bytes": value.get("bytes" ), "path": value.get("path" )}
									else:
												raise ValueError(
												    F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			dict	,	__lowerCamelCase      :			Optional[Dict[str, Union[str, bool, None]]] = None )      ->				dict:
									if not self.decode:
												raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )

									A       , A							:					Optional[int]              =						(value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
									if path is None and file is None:
												raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )

									try:
												import librosa
												import soundfile as sf
									except ImportError as err:
												raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err

									A							:					List[str]              =						xsplitext(__lowerCamelCase )[1][1:].lower() if path is not None else None
									if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
												raise RuntimeError(
												    "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
												    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
									elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
												raise RuntimeError(
												    "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
												    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )

									if file is None:
												A							:					int              =						token_per_repo_id or {}
												A							:					Tuple              =						path.split("::" )[-1]
												try:
															A							:					List[Any]              =						string_to_dict(__lowerCamelCase	,	config.HUB_DATASETS_URL )["repo_id"]
															A							:					Tuple              =						token_per_repo_id[repo_id]
												except (ValueError, KeyError):
															A							:					int              =						None

												with xopen(__lowerCamelCase	,	"rb"	,	use_auth_token=__lowerCamelCase ) as f:
															A       , A							:					List[str]              =						sf.read(__lowerCamelCase )

									else:
												A       , A							:					Tuple              =						sf.read(__lowerCamelCase )

									A							:					List[str]              =						array.T
									if self.mono:
												A							:					Optional[int]              =						librosa.to_mono(__lowerCamelCase )
									if self.sampling_rate and self.sampling_rate != sampling_rate:
												A							:					Dict              =						librosa.resample(__lowerCamelCase	,	orig_sr=__lowerCamelCase	,	target_sr=self.sampling_rate )
												A							:					str              =						self.sampling_rate

									return {"path": path, "array": array, "sampling_rate": sampling_rate}







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Union["FeatureType", Dict[str, "FeatureType"]]:
									from .features import Value

									if self.decode:
												raise ValueError("Cannot flatten a decoded Audio feature." )
									return {
									    "bytes": Value("binary" ),
									    "path": Value("string" ),
									}







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[pa.StringArray, pa.StructArray] )      ->				pa.StructArray:
									if pa.types.is_string(storage.type ):
												A							:					int              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.binary() )
												A							:					Any              =						pa.StructArray.from_arrays([bytes_array, storage]	,	["bytes", "path"]	,	mask=storage.is_null() )
									elif pa.types.is_binary(storage.type ):
												A							:					Any              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.string() )
												A							:					Union[str, Any]              =						pa.StructArray.from_arrays([storage, path_array]	,	["bytes", "path"]	,	mask=storage.is_null() )
									elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
												A							:					List[str]              =						pa.array([Audio().encode_example(__lowerCamelCase ) if x is not None else None for x in storage.to_pylist()] )
									elif pa.types.is_struct(storage.type ):
												if storage.type.get_field_index("bytes" ) >= 0:
															A							:					Any              =						storage.field("bytes" )
												else:
															A							:					Dict              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.binary() )
												if storage.type.get_field_index("path" ) >= 0:
															A							:					Optional[Any]              =						storage.field("path" )
												else:
															A							:					Union[str, Any]              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.string() )
												A							:					int              =						pa.StructArray.from_arrays([bytes_array, path_array]	,	["bytes", "path"]	,	mask=storage.is_null() )
									return array_cast(__lowerCamelCase	,	self.pa_type )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			pa.StructArray )      ->				pa.StructArray:

									@no_op_if_value_is_null
									def path_to_bytes(__lowerCamelCase      :			str ):
												with xopen(__lowerCamelCase	,	"rb" ) as f:
															A							:					int              =						f.read()
												return bytes_

									A							:					str              =						pa.array(
									    [
									        (path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
									        for x in storage.to_pylist()
									    ]	,	type=pa.binary()	,	)
									A							:					Optional[int]              =						pa.array(
									    [os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field("path" ).to_pylist()]	,	type=pa.string()	,	)
									A							:					Tuple              =						pa.StructArray.from_arrays([bytes_array, path_array]	,	["bytes", "path"]	,	mask=bytes_array.is_null() )
									return array_cast(__lowerCamelCase	,	self.pa_type )




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Any              =						self.get_scheduler_config(**__lowerCamelCase )
												A							:					int              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )
												# copy over dummy past residuals
												A							:					Tuple              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					Dict              =						scheduler_class.from_pretrained(__lowerCamelCase )
															new_scheduler.set_timesteps(__lowerCamelCase )
															# copy over dummy past residuals
															A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

												# copy over dummy past residuals (must be after setting timesteps)
												A							:					Optional[int]              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					str              =						scheduler_class.from_pretrained(__lowerCamelCase )
															# copy over dummy past residuals
															new_scheduler.set_timesteps(__lowerCamelCase )

															# copy over dummy past residual (must be after setting timesteps)
															A							:					Optional[Any]              =						dummy_past_residuals[:]

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												if num_inference_steps is not None and hasattr(__lowerCamelCase	,	"set_timesteps" ):
															scheduler.set_timesteps(__lowerCamelCase )
												elif num_inference_steps is not None and not hasattr(__lowerCamelCase	,	"set_timesteps" ):
															A							:					List[str]              =						num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												self.assertEqual(output_a.shape	,	sample.shape )
												self.assertEqual(output_a.shape	,	output_a.shape )

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						scheduler.step_plms(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												self.assertEqual(output_a.shape	,	sample.shape )
												self.assertEqual(output_a.shape	,	output_a.shape )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.set_timesteps(__lowerCamelCase )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.step_plms(self.dummy_sample	,	1	,	self.dummy_sample ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3