from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for part_id in partition_order:
            _lowerCamelCase			    =				df.where(F'SPARK_PARTITION_ID() = {part_id}'		).collect()
            for row_idx, row in enumerate(__UpperCAmelCase		):
                  expected_row_ids_and_row_dicts.append((F'{part_id}_{row_idx}', row.asDict())		)
      return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(100		).repartition(1		)
      _lowerCamelCase			    =				Spark(__UpperCAmelCase		)
      # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
      # that each partition can hold 2 rows.
      spark_builder._repartition_df_if_needed(max_shard_size=16		)
      # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
      assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(10		).repartition(2		)
      _lowerCamelCase			    =				[1, 0]
      _lowerCamelCase			    =				_generate_iterable_examples(__UpperCAmelCase ,						__UpperCAmelCase		)  # Reverse the partitions.
      _lowerCamelCase			    =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCAmelCase ,						__UpperCAmelCase		)

      for i, (row_id, row_dict) in enumerate(generate_fn()		):
            _lowerCamelCase    ,					_lowerCamelCase			    =				expected_row_ids_and_row_dicts[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(10		).repartition(1		)
      _lowerCamelCase			    =				SparkExamplesIterable(__UpperCAmelCase		)
      assert it.n_shards == 1
      for i, (row_id, row_dict) in enumerate(__UpperCAmelCase		):
            assert row_id == F'0_{i}'
            assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(30		).repartition(3		)
      # Mock the generator so that shuffle reverses the partition indices.
      with patch('''numpy.random.Generator'''		) as generator_mock:
            _lowerCamelCase			    =				lambda __UpperCAmelCase		: x.reverse()
            _lowerCamelCase			    =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCAmelCase ,						[2, 1, 0]		)

            _lowerCamelCase			    =				SparkExamplesIterable(__UpperCAmelCase		).shuffle_data_sources(__UpperCAmelCase		)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(__UpperCAmelCase		):
                  _lowerCamelCase    ,					_lowerCamelCase			    =				expected_row_ids_and_row_dicts[i]
                  assert row_id == expected_row_id
                  assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(20		).repartition(4		)

      # Partitions 0 and 2
      _lowerCamelCase			    =				SparkExamplesIterable(__UpperCAmelCase		).shard_data_sources(worker_id=0 ,						num_workers=2		)
      assert shard_it_a.n_shards == 2
      _lowerCamelCase			    =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCAmelCase ,						[0, 2]		)
      for i, (row_id, row_dict) in enumerate(__UpperCAmelCase		):
            _lowerCamelCase    ,					_lowerCamelCase			    =				expected_row_ids_and_row_dicts_a[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict

      # Partitions 1 and 3
      _lowerCamelCase			    =				SparkExamplesIterable(__UpperCAmelCase		).shard_data_sources(worker_id=1 ,						num_workers=2		)
      assert shard_it_a.n_shards == 2
      _lowerCamelCase			    =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCAmelCase ,						[1, 3]		)
      for i, (row_id, row_dict) in enumerate(__UpperCAmelCase		):
            _lowerCamelCase    ,					_lowerCamelCase			    =				expected_row_ids_and_row_dicts_a[i]
            assert row_id == expected_row_id
            assert row_dict == expected_row_dict




@require_not_windows
@require_dill_gt_0_3_2
def       __magic_name__(  )						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
      _lowerCamelCase			    =				spark.range(100		).repartition(1		)
      _lowerCamelCase			    =				Spark(__UpperCAmelCase		)
      # Choose a small max_shard_size for maximum partitioning.
      spark_builder._repartition_df_if_needed(max_shard_size=1		)
      # The new number of partitions should not be greater than the number of rows.
      assert spark_builder.df.rdd.getNumPartitions() == 100
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				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _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=A_				,				device=self.device				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _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(A_				,				0						)						).latent_dist.sample(
                           generator=A_						)[0]
                       _lowerCamelCase			    =				self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				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(A_				,				A_				,				torch.tensor(self.scheduler.timesteps[start_step:]						)						)

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

                 if isinstance(self.scheduler				,				A_						):
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
                 else:
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''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(A_						)['''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(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

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

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



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _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(A_						).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(A_				,				A_						)['''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 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)