
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def    A (      lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  np.inf

		def set_batch_size(lowercase				) -> None:
				nonlocal batch_size
				if isinstance(lowercase					, lowercase				):
						UpperCamelCase						=  min(lowercase					, config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
				elif isinstance(lowercase					, lowercase				):
						UpperCamelCase						=  min(lowercase					, config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
				elif isinstance(lowercase					, lowercase				) and feature.dtype == "binary":
						UpperCamelCase						=  min(lowercase					, config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

		_visit(lowercase					, lowercase				)

		return None if batch_size is np.inf else batch_size


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   A_ = False       ,   A_ = False       ,   A_ = None       ,   **A_       ,   )						->      Union[str, Any]:
				"""simple docstring"""
				super().__init__(
				    A_       ,   split=A_       ,   features=A_       ,   cache_dir=A_       ,   keep_in_memory=A_       ,   streaming=A_       ,   num_proc=A_       ,   **A_       ,   )
				UpperCamelCase						=  path_or_paths if isinstance(A_       ,   A_ ) else {self.split: path_or_paths}
				UpperCamelCase						=  _PACKAGED_DATASETS_MODULES['parquet'][1]
				UpperCamelCase						=  Parquet(
				    cache_dir=A_       ,   data_files=A_       ,   features=A_       ,   hash=A_       ,   **A_       ,   )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				# Build iterable dataset
				if self.streaming:
						UpperCamelCase						=  self.builder.as_streaming_dataset(split=self.split )
				# Build regular (map-style) dataset
				else:
						UpperCamelCase						=  None
						UpperCamelCase						=  None
						UpperCamelCase						=  None
						UpperCamelCase						=  None

						self.builder.download_and_prepare(
						    download_config=A_       ,   download_mode=A_       ,   verification_mode=A_       ,   base_path=A_       ,   num_proc=self.num_proc       ,   )
						UpperCamelCase						=  self.builder.as_dataset(
						    split=self.split       ,   verification_mode=A_       ,   in_memory=self.keep_in_memory )
				return dataset






class        lowercase      :



		def __init__(				self       ,   A_       ,   A_       ,   A_ = None       ,   **A_       ,   )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  dataset
				UpperCamelCase						=  path_or_buf
				UpperCamelCase						=  batch_size or get_writer_batch_size(dataset.features )
				UpperCamelCase						=  parquet_writer_kwargs



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

				if isinstance(self.path_or_buf       ,   (str, bytes, os.PathLike) ):
						with open(self.path_or_buf       ,   'wb+' ) as buffer:
								UpperCamelCase						=  self._write(file_obj=A_       ,   batch_size=A_       ,   **self.parquet_writer_kwargs )
				else:
						UpperCamelCase						=  self._write(file_obj=self.path_or_buf       ,   batch_size=A_       ,   **self.parquet_writer_kwargs )
				return written



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   **A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  0
				UpperCamelCase						=  parquet_writer_kwargs.pop('path_or_buf'       ,   A_ )
				UpperCamelCase						=  self.dataset.features.arrow_schema

				UpperCamelCase						=  pq.ParquetWriter(A_       ,   schema=A_       ,   **A_ )

				for offset in logging.tqdm(
				    range(0       ,   len(self.dataset )       ,   A_ )       ,   unit='ba'       ,   disable=not logging.is_progress_bar_enabled()       ,   desc='Creating parquet from Arrow format'       ,   ):
						UpperCamelCase						=  query_table(
						    table=self.dataset._data       ,   key=slice(A_       ,   offset + batch_size )       ,   indices=self.dataset._indices if self.dataset._indices is not None else None       ,   )
						writer.write_table(A_ )
						written += batch.nbytes
				writer.close()
				return written


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

				UpperCamelCase						=  self.dummy_model()
				UpperCamelCase						=  self.dummy_sample_deter
				UpperCamelCase						=  torch.manual_seed(0 )

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

						# 2. predict previous mean of sample x_t-1
						UpperCamelCase						=  scheduler.step(A_       ,   A_       ,   A_       ,   generator=A_ ).prev_sample

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
