




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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: int										=["""vqvae"""]



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , ):
											super().__init__()
											self.register_modules(unet=UpperCAmelCase_       , scheduler=UpperCAmelCase_       , mel=UpperCAmelCase_       , vqvae=UpperCAmelCase_						)



				def       __snake_case		( self						):
											return 50 if isinstance(self.scheduler       , UpperCAmelCase_						) else 10_00



				@torch.no_grad()
				def __call__( self       , UpperCAmelCase_ = 1       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_=True       , ):
											lowerCAmelCase   =		steps or self.get_default_steps()
											self.scheduler.set_timesteps(UpperCAmelCase_						)
											lowerCAmelCase   =		step_generator or generator
											# For backwards compatibility
											if type(self.unet.config.sample_size						) == int:
																		lowerCAmelCase   =		(self.unet.config.sample_size, self.unet.config.sample_size)
											if noise is None:
																		lowerCAmelCase   =		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       , )
											lowerCAmelCase   =		noise
											lowerCAmelCase   =		None

											if audio_file is not None or raw_audio is not None:
																		self.mel.load_audio(UpperCAmelCase_       , UpperCAmelCase_						)
																		lowerCAmelCase   =		self.mel.audio_slice_to_image(UpperCAmelCase_						)
																		lowerCAmelCase   =		np.frombuffer(input_image.tobytes()       , dtype='''uint8'''						).reshape(
																		    (input_image.height, input_image.width)						)
																		lowerCAmelCase   =		(input_image / 2_55) * 2 - 1
																		lowerCAmelCase   =		torch.tensor(input_image[np.newaxis, :, :]       , dtype=torch.float						).to(self.device						)

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

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

																		lowerCAmelCase   =		(
																		    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																		)
																		lowerCAmelCase   =		int(mask_start_secs * pixels_per_second						)
																		lowerCAmelCase   =		int(mask_end_secs * pixels_per_second						)
																		lowerCAmelCase   =		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_						):
																									lowerCAmelCase   =		self.unet(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)['''sample''']
																		else:
																									lowerCAmelCase   =		self.unet(UpperCAmelCase_       , UpperCAmelCase_						)['''sample''']

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

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

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

											lowerCAmelCase   =		(images / 2 + 0.5).clamp(0       , 1						)
											lowerCAmelCase   =		images.cpu().permute(0       , 2       , 3       , 1						).numpy()
											lowerCAmelCase   =		(images * 2_55).round().astype('''uint8'''						)
											lowerCAmelCase   =		list(
											    (Image.fromarray(_[:, :, 0]						) for _ in images)
											    if images.shape[3] == 1
											    else (Image.fromarray(UpperCAmelCase_       , mode='''RGB'''						).convert('''L'''						) for _ in images)						)

											lowerCAmelCase   =		[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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = 50						):
											assert isinstance(self.scheduler       , UpperCAmelCase_						)
											self.scheduler.set_timesteps(UpperCAmelCase_						)
											lowerCAmelCase   =		np.array(
											    [np.frombuffer(image.tobytes()       , dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
											lowerCAmelCase   =		(sample / 2_55) * 2 - 1
											lowerCAmelCase   =		torch.Tensor(UpperCAmelCase_						).to(self.device						)

											for t in self.progress_bar(torch.flip(self.scheduler.timesteps       , (0,)						)						):
																		lowerCAmelCase   =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																		lowerCAmelCase   =		self.scheduler.alphas_cumprod[t]
																		lowerCAmelCase   =		(
																		    self.scheduler.alphas_cumprod[prev_timestep]
																		    if prev_timestep >= 0
																		    else self.scheduler.final_alpha_cumprod
																		)
																		lowerCAmelCase   =		1 - alpha_prod_t
																		lowerCAmelCase   =		self.unet(UpperCAmelCase_       , UpperCAmelCase_						)['''sample''']
																		lowerCAmelCase   =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
																		lowerCAmelCase   =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																		lowerCAmelCase   =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

											return sample






				@staticmethod
				def       __snake_case		( UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		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_						)









import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_						=datasets.utils.logging.get_logger(__name__)


@dataclass
class 							__UpperCamelCase			( datasets.BuilderConfig  ):





				'''simple docstring'''



				__a							: Optional[datasets.Features]										=None
				__a							: str										="utf-8"
				__a							: Optional[str]										=None
				__a							: Optional[str]										=None
				__a							: bool										=True  # deprecated
				__a							: Optional[int]										=None  # deprecated
				__a							: int										=1_0 << 2_0  # 10MB
				__a							: Optional[bool]										=None




class 							__UpperCamelCase			( datasets.ArrowBasedBuilder  ):





				'''simple docstring'''



				__a							: str										=JsonConfig



				def       __snake_case		( self						):
											if self.config.block_size is not None:
																		logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''						)
																		lowerCAmelCase   =		self.config.block_size
											if self.config.use_threads is not True:
																		logger.warning(
																		    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''						)
											if self.config.newlines_in_values is not None:
																		raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''						)
											return datasets.DatasetInfo(features=self.config.features						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											if not self.config.data_files:
																		raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""						)
											lowerCAmelCase   =		dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCAmelCase_       , (str, list, tuple)						):
																		lowerCAmelCase   =		data_files
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs={'''files''': files}						)]
											lowerCAmelCase   =		[]
											for split_name, files in data_files.items():
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		splits.append(datasets.SplitGenerator(name=UpperCAmelCase_       , gen_kwargs={'''files''': files}						)						)
											return splits



				def       __snake_case		( self       , UpperCAmelCase_						):
											if self.config.features is not None:
																		# adding missing columns
																		for column_name in set(self.config.features						) - set(pa_table.column_names						):
																									lowerCAmelCase   =		self.config.features.arrow_schema.field(UpperCAmelCase_						).type
																									lowerCAmelCase   =		pa_table.append_column(UpperCAmelCase_       , pa.array([None] * len(UpperCAmelCase_						)       , type=UpperCAmelCase_						)						)
																		# more expensive cast to support nested structures with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		lowerCAmelCase   =		table_cast(UpperCAmelCase_       , self.config.features.arrow_schema						)
											return pa_table






				def       __snake_case		( self       , UpperCAmelCase_						):
											for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_						)						):
																		# If the file is one json object and if we need to look at the list of items in one specific field
																		if self.config.field is not None:
																									with open(UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																lowerCAmelCase   =		json.load(UpperCAmelCase_						)

																									# We keep only the field we are interested in
																									lowerCAmelCase   =		dataset[self.config.field]

																									# We accept two format: a list of dicts or a dict of lists
																									if isinstance(UpperCAmelCase_       , (list, tuple)						):
																																lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																									else:
																																lowerCAmelCase   =		dataset
																									lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																									yield file_idx, self._cast_table(UpperCAmelCase_						)

																		# If the file has one json object per line
																		else:
																									with open(UpperCAmelCase_       , '''rb'''						) as f:
																																lowerCAmelCase   =		0
																																# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																																# Set a default minimum value of 16kB if the chunk size is really small
																																lowerCAmelCase   =		max(self.config.chunksize // 32       , 16 << 10						)
																																lowerCAmelCase   =		(
																																    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																																)
																																while True:
																																							lowerCAmelCase   =		f.read(self.config.chunksize						)
																																							if not batch:
																																														break
																																							# Finish current line
																																							try:
																																														batch += f.readline()
																																							except (AttributeError, io.UnsupportedOperation):
																																														batch += readline(UpperCAmelCase_						)
																																							# PyArrow only accepts utf-8 encoded bytes
																																							if self.config.encoding != "utf-8":
																																														lowerCAmelCase   =		batch.decode(self.config.encoding       , errors=UpperCAmelCase_						).encode('''utf-8'''						)
																																							try:
																																														while True:
																																																					try:
																																																												lowerCAmelCase   =		paj.read_json(
																																																												    io.BytesIO(UpperCAmelCase_						)       , read_options=paj.ReadOptions(block_size=UpperCAmelCase_						)						)
																																																												break
																																																					except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																												if (
																																																												    isinstance(UpperCAmelCase_       , pa.ArrowInvalid						)
																																																												    and "straddling" not in str(UpperCAmelCase_						)
																																																												    or block_size > len(UpperCAmelCase_						)
																																																												):
																																																																			raise
																																																												else:
																																																																			# Increase the block size in case it was too small.
																																																																			# The block size will be reset for the next file.
																																																																			logger.debug(
																																																																			    F"""Batch of {len(UpperCAmelCase_						)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""						)
																																																																			block_size *= 2
																																							except pa.ArrowInvalid as e:
																																														try:
																																																					with open(
																																																					    UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																																												lowerCAmelCase   =		json.load(UpperCAmelCase_						)
																																														except json.JSONDecodeError:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise e
																																														# If possible, parse the file as a list of json objects and exit the loop
																																														if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):  # list is the only sequence type supported in JSON
																																																					try:
																																																												lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																																												lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																																																												lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																																																					except (pa.ArrowInvalid, AttributeError) as e:
																																																												logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																												raise ValueError(F"""Not able to read records in the JSON file at {file}."""						) from None
																																																					yield file_idx, self._cast_table(UpperCAmelCase_						)
																																																					break
																																														else:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise ValueError(
																																																					    F"""Not able to read records in the JSON file at {file}. """
																																																					    F"""You should probably indicate the field of the JSON file containing your records. """
																																																					    F"""This JSON file contain the following fields: {str(list(dataset.keys()						)						)}. """
																																																					    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """						) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																							yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase_						)
																																							batch_idx += 1



