






'''simple docstring'''
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def  _a(  UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      assert isinstance(UpperCamelCase__,	UpperCamelCase__							)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''',	[False, True]							)
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				Tuple									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=ParquetDatasetReader(UpperCamelCase__,	cache_dir=UpperCamelCase__,	keep_in_memory=UpperCamelCase__							).read()
      _check_parquet_dataset(UpperCamelCase__,	UpperCamelCase__							)





@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				Dict									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      SCREAMING_SNAKE_CASE__     :				Dict									=features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE__     :				str									=(
          Features({feature: Value(UpperCamelCase__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      SCREAMING_SNAKE_CASE__     :				Any									=ParquetDatasetReader(UpperCamelCase__,	features=UpperCamelCase__,	cache_dir=UpperCamelCase__							).read()
      _check_parquet_dataset(UpperCamelCase__,	UpperCamelCase__							)





@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				Any									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      SCREAMING_SNAKE_CASE__     :				int									=ParquetDatasetReader(UpperCamelCase__,	cache_dir=UpperCamelCase__,	split=UpperCamelCase__							).read()
      _check_parquet_dataset(UpperCamelCase__,	UpperCamelCase__							)
      assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''',	[str, list]							)
def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Dict,	UpperCamelCase__				:						List[Any]							):




      '''simple docstring'''






      if issubclass(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parquet_path
      elif issubclass(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				Tuple									=[parquet_path]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=ParquetDatasetReader(UpperCamelCase__,	cache_dir=UpperCamelCase__							).read()
      _check_parquet_dataset(UpperCamelCase__,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str]=("train",)							):




      '''simple docstring'''






      assert isinstance(UpperCamelCase__,	UpperCamelCase__							)
      for split in splits:
            SCREAMING_SNAKE_CASE__     :				Tuple									=dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 3
            assert dataset.column_names == ["col_1", "col_2", "col_3"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''',	[False, True]							)
def  _a(  UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						str,	UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				int									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE__     :				List[Any]									=ParquetDatasetReader(
                {'''train''': parquet_path},	cache_dir=UpperCamelCase__,	keep_in_memory=UpperCamelCase__							).read()
      _check_parquet_datasetdict(UpperCamelCase__,	UpperCamelCase__							)





@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						int,	UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				List[str]									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      SCREAMING_SNAKE_CASE__     :				Dict									=features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE__     :				List[Any]									=(
          Features({feature: Value(UpperCamelCase__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      SCREAMING_SNAKE_CASE__     :				List[Any]									=ParquetDatasetReader({'''train''': parquet_path},	features=UpperCamelCase__,	cache_dir=UpperCamelCase__							).read()
      _check_parquet_datasetdict(UpperCamelCase__,	UpperCamelCase__							)





@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def  _a(  UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Any,	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      if split:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									={split: parquet_path}
      else:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''train'''
            SCREAMING_SNAKE_CASE__     :				str									={'''train''': parquet_path, '''test''': parquet_path}
      SCREAMING_SNAKE_CASE__     :				Any									=tmp_path / '''cache'''
      SCREAMING_SNAKE_CASE__     :				Optional[int]									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=ParquetDatasetReader(UpperCamelCase__,	cache_dir=UpperCamelCase__							).read()
      _check_parquet_datasetdict(UpperCamelCase__,	UpperCamelCase__,	splits=list(path.keys()							)							)
      assert all(dataset[split].split == split for split in path.keys()							)





def  _a(  UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=ParquetDatasetWriter(UpperCamelCase__,	tmp_path / '''foo.parquet'''							)
      assert writer.write() > 0
      SCREAMING_SNAKE_CASE__     :				List[Any]									=pq.ParquetFile(tmp_path / '''foo.parquet'''							)
      SCREAMING_SNAKE_CASE__     :				Any									=pf.read()
      assert dataset.data.table == output_table





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=str(shared_datadir / '''test_image_rgb.jpg'''							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									={'''image''': [image_path]}
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Features({'''image''': Image()}							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=Dataset.from_dict(UpperCamelCase__,	features=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=ParquetDatasetWriter(UpperCamelCase__,	tmp_path / '''foo.parquet'''							)
      assert writer.write() > 0

      SCREAMING_SNAKE_CASE__     :				List[Any]									=Dataset.from_parquet(str(tmp_path / '''foo.parquet'''							)							)
      assert dataset.features == reloaded_dataset.features

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ParquetDatasetReader(str(tmp_path / '''foo.parquet'''							),	streaming=UpperCamelCase__							).read()
      assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    '''feature, expected''',	[
        (Features({'''foo''': Value('''int32'''							)}							), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ],	)
def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      assert get_writer_batch_size(UpperCamelCase__							) == expected







'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""vqvae"""]




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




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return 50 if isinstance(self.scheduler					,      __lowercase      ) else 10_00




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

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

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

             if start_step > 0:
                   SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      self.scheduler.timesteps[start_step - 1]      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=int(mask_start_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=int(mask_end_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      torch.tensor(self.scheduler.timesteps[start_step:]      )      )

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]      )      ):
             if isinstance(self.unet					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase					,      __lowercase      )['''sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase      )['''sample''']

             if isinstance(self.scheduler					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      eta=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				Any									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']

             if mask is not None:
                   if mask_start > 0:
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=mask[:, step, :, :mask_start]
                   if mask_end > 0:
                         SCREAMING_SNAKE_CASE__     :				List[str]									=mask[:, step, :, -mask_end:]

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

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

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[self.mel.image_to_audio(__lowercase      ) for _ in images]
       if not return_dict:
             return images, (self.mel.get_sample_rate(), audios)

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




 @torch.no_grad()
 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Image.Image]					,      __lowercase	:							int = 50      )	->						np.ndarray:
       assert isinstance(self.scheduler					,      __lowercase      )
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array(
           [np.frombuffer(image.tobytes()					,      dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
       SCREAMING_SNAKE_CASE__     :				str									=(sample / 2_55) * 2 - 1
       SCREAMING_SNAKE_CASE__     :				str									=torch.Tensor(__lowercase      ).to(self.device      )

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

       return sample




 @staticmethod
 def      __magic_name__	(      __lowercase	:							torch.Tensor					,      __lowercase	:							torch.Tensor					,      __lowercase	:							float      )	->						torch.Tensor:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=acos(torch.dot(torch.flatten(__lowercase      )					,      torch.flatten(__lowercase      )      ) / torch.norm(__lowercase      ) / torch.norm(__lowercase      )      )
       return sin((1 - alpha) * theta      ) * xa / sin(__lowercase      ) + sin(alpha * theta      ) * xa / sin(__lowercase      )