



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			str:
							UpperCAmelCase_=       pa.array(TypedSequence([1, 2, 3]					)					)
							self.assertEqual(arr.type						,  pa.intaa()					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Union[str, Any]:
							with self.assertRaises(__UpperCAmelCase					):
													UpperCAmelCase_=       pa.array(TypedSequence([1, 2, 3]					)						,  type=pa.intaa()					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[Any]:
							with self.assertRaises(__UpperCAmelCase					):
													UpperCAmelCase_=       pa.array(TypedSequence([1, 2, 3]						,  try_type=Value("""bool"""					)						,  type=Value("""int64"""					)					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			str:
							UpperCAmelCase_=       pa.array(TypedSequence([1, 2, 3]						,  type=Value("""int32"""					)					)					)
							self.assertEqual(arr.type						,  pa.intaa()					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
							with self.assertRaises((TypeError, pa.lib.ArrowInvalid)					):
													UpperCAmelCase_=       pa.array(TypedSequence(["""foo""", """bar"""]						,  type=Value("""int64"""					)					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							UpperCAmelCase_=       pa.array(TypedSequence([1, 2, 3]						,  try_type=Value("""int32"""					)					)					)
							self.assertEqual(arr.type						,  pa.intaa()					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       pa.array(TypedSequence(["""foo""", """bar"""]						,  try_type=Value("""int64"""					)					)					)
							self.assertEqual(arr.type						,  pa.string()					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			int:
							UpperCAmelCase_=       pa.array(TypedSequence([[[1, 2, 3]]]						,  type=ArrayaD((1, 3)						,  """int64"""					)					)					)
							self.assertEqual(arr.type						,  ArrayaDExtensionType((1, 3)						,  """int64"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[Any]:
							with self.assertRaises((TypeError, pa.lib.ArrowInvalid)					):
													UpperCAmelCase_=       pa.array(TypedSequence(["""foo""", """bar"""]						,  type=ArrayaD((1, 3)						,  """int64"""					)					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Dict:
							UpperCAmelCase_=       pa.array(TypedSequence([[[1, 2, 3]]]						,  try_type=ArrayaD((1, 3)						,  """int64"""					)					)					)
							self.assertEqual(arr.type						,  ArrayaDExtensionType((1, 3)						,  """int64"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       pa.array(TypedSequence(["""foo""", """bar"""]						,  try_type=ArrayaD((1, 3)						,  """int64"""					)					)					)
							self.assertEqual(arr.type						,  pa.string()					)




	@require_pil
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							import PIL.Image

							UpperCAmelCase_=       PIL.Image.fromarray(np.arange(10						,  dtype=np.uinta					).reshape(2						,  5					)					)
							with patch(
							    """datasets.arrow_writer.cast_to_python_objects"""						,  side_effect=__UpperCAmelCase					) as mock_cast_to_python_objects:
													UpperCAmelCase_=       pa.array(TypedSequence([{"""path""": None, """bytes""": B"""image_bytes"""}, pil_image]						,  type=Image()					)					)
													UpperCAmelCase_,       UpperCAmelCase_=       mock_cast_to_python_objects.call_args_list[-1]
													self.assertIn("""optimize_list_casting"""						,  __UpperCAmelCase					)
													self.assertFalse(kwargs["""optimize_list_casting"""]					)


def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : int    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferReader(lowerCAmelCase_    ) if isinstance(lowerCAmelCase_      ,pa.Buffer    ) else pa.memory_map(lowerCAmelCase_    )
						UpperCAmelCase_=       pa.ipc.open_stream(lowerCAmelCase_    )
						UpperCAmelCase_=       f.read_all()
						assert len(pa_table.to_batches()    ) == expected_num_chunks
						assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
						del pa_table


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 1, 10]    )
@pytest.mark.parametrize(
    """fields"""      ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]    )
def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						UpperCAmelCase_=       pa.schema(lowerCAmelCase_    ) if fields else None
						with ArrowWriter(stream=lowerCAmelCase_      ,schema=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_    ) as writer:
												writer.write({"""col_1""": """foo""", """col_2""": 1}    )
												writer.write({"""col_1""": """bar""", """col_2""": 2}    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												UpperCAmelCase_=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
						assert writer._schema == pa.schema(lowerCAmelCase_      ,metadata=writer._schema.metadata    )
						_check_output(output.getvalue()      ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )


def 	__a					(   )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						UpperCAmelCase_=       Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]    )}    )
						with ArrowWriter(stream=lowerCAmelCase_      ,features=lowerCAmelCase_    ) as writer:
												writer.write({"""labels""": 0}    )
												writer.write({"""labels""": 1}    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == features.arrow_schema
						assert writer._schema.metadata == features.arrow_schema.metadata
						UpperCAmelCase_=       pa.BufferReader(output.getvalue()    )
						UpperCAmelCase_=       pa.ipc.open_stream(lowerCAmelCase_    )
						UpperCAmelCase_=       f.read_all()
						UpperCAmelCase_=       pa_table.schema
						assert pa_table.num_rows == 2
						assert schema == features.arrow_schema
						assert schema.metadata == features.arrow_schema.metadata
						assert features == Features.from_arrow_schema(lowerCAmelCase_    )


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 1, 10]    )
def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						with ArrowWriter(
						    stream=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_      ,hash_salt="""split_name"""      ,check_duplicates=lowerCAmelCase_      ,) as writer:
												with pytest.raises(lowerCAmelCase_    ):
																		writer.write({"""col_1""": """foo""", """col_2""": 1}      ,key=[1, 2]    )
																		UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 2, 10]    )
def 	__a					(   lowerCAmelCase_       : Tuple    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						with ArrowWriter(
						    stream=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_      ,hash_salt="""split_name"""      ,check_duplicates=lowerCAmelCase_      ,) as writer:
												with pytest.raises(lowerCAmelCase_    ):
																		writer.write({"""col_1""": """foo""", """col_2""": 1}      ,key=10    )
																		writer.write({"""col_1""": """bar""", """col_2""": 2}      ,key=10    )
																		UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 2, 10]    )
def 	__a					(   lowerCAmelCase_       : Dict    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						with ArrowWriter(
						    stream=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_      ,hash_salt="""split_name"""      ,check_duplicates=lowerCAmelCase_      ,) as writer:
												writer.write({"""col_1""": """foo""", """col_2""": 1}      ,key=1    )
												writer.write({"""col_1""": """bar""", """col_2""": 2}      ,key=2    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						_check_output(output.getvalue()      ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 1, 10]    )
@pytest.mark.parametrize(
    """fields"""      ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]    )
def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Dict    )				->			Any:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						UpperCAmelCase_=       pa.schema(lowerCAmelCase_    ) if fields else None
						with ArrowWriter(stream=lowerCAmelCase_      ,schema=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_    ) as writer:
												writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}    )
												writer.write_batch({"""col_1""": [], """col_2""": []}    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												UpperCAmelCase_=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
						assert writer._schema == pa.schema(lowerCAmelCase_      ,metadata=writer._schema.metadata    )
						_check_output(output.getvalue()      ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 1, 10]    )
@pytest.mark.parametrize(
    """fields"""      ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]    )
def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Optional[int]    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						UpperCAmelCase_=       pa.schema(lowerCAmelCase_    ) if fields else None
						with ArrowWriter(stream=lowerCAmelCase_      ,schema=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_    ) as writer:
												writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}    )    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												UpperCAmelCase_=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
						assert writer._schema == pa.schema(lowerCAmelCase_      ,metadata=writer._schema.metadata    )
						_check_output(output.getvalue()      ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )


@pytest.mark.parametrize("""writer_batch_size"""      ,[None, 1, 10]    )
@pytest.mark.parametrize(
    """fields"""      ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]    )
def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Union[str, Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						UpperCAmelCase_=       pa.schema(lowerCAmelCase_    ) if fields else None
						with ArrowWriter(stream=lowerCAmelCase_      ,schema=lowerCAmelCase_      ,writer_batch_size=lowerCAmelCase_    ) as writer:
												writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}    )    )
												writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}    )    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												UpperCAmelCase_=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
						assert writer._schema == pa.schema(lowerCAmelCase_      ,metadata=writer._schema.metadata    )
						_check_output(output.getvalue()      ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )


def 	__a					(   )				->			Union[str, Any]:






						'''simple docstring'''



						with tempfile.TemporaryDirectory() as tmp_dir:
												UpperCAmelCase_=       {"""col_1""": pa.string(), """col_2""": pa.intaa()}
												UpperCAmelCase_=       os.path.join(lowerCAmelCase_      ,"""test.arrow"""    )
												with ArrowWriter(path=lowerCAmelCase_      ,schema=pa.schema(lowerCAmelCase_    )    ) as writer:
																		writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}    )
																		UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
												assert num_examples == 2
												assert num_bytes > 0
												assert writer._schema == pa.schema(lowerCAmelCase_      ,metadata=writer._schema.metadata    )
												_check_output(lowerCAmelCase_      ,1    )


def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			str:






						'''simple docstring'''



						if pa.types.is_list(lowerCAmelCase_    ):
												return get_base_dtype(arr_type.value_type    )
						else:
												return arr_type


def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    )				->			Optional[int]:






						'''simple docstring'''



						if isinstance(lst[0]      ,lowerCAmelCase_    ):
												change_first_primitive_element_in_list(lst[0]      ,lowerCAmelCase_    )
						else:
												UpperCAmelCase_=       value


@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""      ,[(None, pa.intaa()), (Value("""int32"""    ), pa.intaa())]    )
@pytest.mark.parametrize("""sequence"""      ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]    )
def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Dict    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.array(TypedSequence(lowerCAmelCase_      ,optimized_int_type=lowerCAmelCase_    )    )
						assert get_base_dtype(arr.type    ) == expected_dtype


@pytest.mark.parametrize(
    """col, expected_dtype"""      ,[
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]      ,)
@pytest.mark.parametrize("""sequence"""      ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]    )
def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Any    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.array(OptimizedTypedSequence(lowerCAmelCase_      ,col=lowerCAmelCase_    )    )
						assert get_base_dtype(arr.type    ) == expected_dtype

						# not in range
						if col != "other":
												# avoids errors due to in-place modifications
												UpperCAmelCase_=       copy.deepcopy(lowerCAmelCase_    )
												UpperCAmelCase_=       np.iinfo(expected_dtype.to_pandas_dtype()    ).max + 1
												change_first_primitive_element_in_list(lowerCAmelCase_      ,lowerCAmelCase_    )
												UpperCAmelCase_=       pa.array(OptimizedTypedSequence(lowerCAmelCase_      ,col=lowerCAmelCase_    )    )
												assert get_base_dtype(arr.type    ) == pa.intaa()


@pytest.mark.parametrize("""raise_exception"""      ,[False, True]    )
def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       str(tmp_path / """dataset-train.arrow"""    )
						try:
												with ArrowWriter(path=lowerCAmelCase_    ) as writer:
																		if raise_exception:
																								raise pa.lib.ArrowInvalid()
																		else:
																								writer.stream.close()
						except pa.lib.ArrowInvalid:
												pass
						finally:
												assert writer.stream.closed


def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       """mock://dataset-train.arrow"""
						with ArrowWriter(path=lowerCAmelCase_      ,storage_options=mockfs.storage_options    ) as writer:
												assert isinstance(writer._fs      ,type(lowerCAmelCase_    )    )
												assert writer._fs.storage_options == mockfs.storage_options
												writer.write({"""col_1""": """foo""", """col_2""": 1}    )
												writer.write({"""col_1""": """bar""", """col_2""": 2}    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert mockfs.exists(lowerCAmelCase_    )


def 	__a					(   )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.BufferOutputStream()
						with ParquetWriter(stream=lowerCAmelCase_    ) as writer:
												writer.write({"""col_1""": """foo""", """col_2""": 1}    )
												writer.write({"""col_1""": """bar""", """col_2""": 2}    )
												UpperCAmelCase_,       UpperCAmelCase_=       writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						UpperCAmelCase_=       pa.BufferReader(output.getvalue()    )
						UpperCAmelCase_=       pq.read_table(lowerCAmelCase_    )
						assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("""embed_local_files"""      ,[False, True]    )
def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






						'''simple docstring'''



						import PIL.Image

						UpperCAmelCase_=       str(tmp_path / """test_image_rgb.jpg"""    )
						PIL.Image.fromarray(np.zeros((5, 5)      ,dtype=np.uinta    )    ).save(lowerCAmelCase_      ,format="""png"""    )
						UpperCAmelCase_=       pa.BufferOutputStream()
						with ParquetWriter(
						    stream=lowerCAmelCase_      ,features=Features({"""image""": Image()}    )      ,embed_local_files=lowerCAmelCase_    ) as writer:
												writer.write({"""image""": image_path}    )
												writer.finalize()
						UpperCAmelCase_=       pa.BufferReader(output.getvalue()    )
						UpperCAmelCase_=       pq.read_table(lowerCAmelCase_    )
						UpperCAmelCase_=       pa_table.to_pydict()
						if embed_local_files:
												assert isinstance(out["""image"""][0]["""path"""]      ,lowerCAmelCase_    )
												with open(lowerCAmelCase_      ,"""rb"""    ) as f:
																		assert out["image"][0]["bytes"] == f.read()
						else:
												assert out["image"][0]["path"] == image_path
												assert out["image"][0]["bytes"] is None



def 	__a					(   )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       pa.schema([pa.field("""col_1"""      ,pa.string()      ,nullable=lowerCAmelCase_    )]    )
						UpperCAmelCase_=       pa.BufferOutputStream()
						with ArrowWriter(stream=lowerCAmelCase_    ) as writer:
												writer._build_writer(inferred_schema=lowerCAmelCase_    )
												assert writer._schema == pa.schema([pa.field("""col_1"""      ,pa.string()    )]    )









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




