




'''simple docstring'''



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase_							:	Optional[Any]	     =		transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any]						)-> Union[str, Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     __snake_case            =	[trans(img.convert('''RGB'''						)						) for img in image]
     __snake_case            =	torch.stack(_lowerCamelCase						)
     return image







class      lowerCAmelCase (			__lowerCAmelCase):


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



            '''simple docstring'''




            super().__init__()

            # make sure scheduler can always be converted to DDIM
            __snake_case            =	DDIMScheduler.from_config(scheduler.config	)

            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            if strength < 0 or strength > 1:
                 raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	min(int(num_inference_steps * strength	)     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	max(num_inference_steps - init_timestep     ,	0	)
            __snake_case            =	self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Tuple:



            '''simple docstring'''




            if not isinstance(__SCREAMING_SNAKE_CASE     ,	(torch.Tensor, PIL.Image.Image, list)	):
                 raise ValueError(
                     F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE	)}'''	)

            __snake_case            =	image.to(device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)

            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE	)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            __snake_case            =	init_latents.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=__SCREAMING_SNAKE_CASE     ,	dtype=__SCREAMING_SNAKE_CASE	)

            # get latents
            print('''add noise to latents at timestep'''     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	init_latents

            return latents




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 0.8     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 50     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            self.check_inputs(__SCREAMING_SNAKE_CASE	)

            # 2. Preprocess image
            __snake_case            =	preprocess(__SCREAMING_SNAKE_CASE	)

            # 3. set timesteps
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	device=self.device	)
            __snake_case       ,     __snake_case            =	self.get_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	timesteps[:1].repeat(__SCREAMING_SNAKE_CASE	)

            # 4. Prepare latent variables
            __snake_case            =	self.prepare_latents(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.unet.dtype     ,	self.device     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	latents

            # 5. Denoising loop
            for t in self.progress_bar(__SCREAMING_SNAKE_CASE	):
                 # 1. predict noise model_output
                 __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample

                 # 2. predict previous mean of image x_t-1 and add variance depending on eta
                 # eta corresponds to η in paper and should be between [0, 1]
                 # do x_t -> x_t-1
                 __snake_case            =	self.scheduler.step(
                     __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	eta=__SCREAMING_SNAKE_CASE     ,	use_clipped_model_output=__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	).prev_sample

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image, latent_timestep.item())

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)












'''simple docstring'''



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       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[Any]						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     for part_id in partition_order:
          __snake_case            =	df.where(f'''SPARK_PARTITION_ID() = {part_id}'''						).collect()
          for row_idx, row in enumerate(_lowerCamelCase						):
               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       _UpperCamelCase			()-> Any:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # 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       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





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

     for i, (row_id, row_dict) in enumerate(generate_fn()						):
          __snake_case       ,     __snake_case            =	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       _UpperCamelCase			()-> int:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(10						).repartition(1						)
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





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

          __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shuffle_data_sources(_lowerCamelCase						)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
               __snake_case       ,     __snake_case            =	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       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





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

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

     # Partitions 1 and 3
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=1 ,						num_workers=2						)
     assert shard_it_a.n_shards == 2
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[1, 3]						)
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          __snake_case       ,     __snake_case            =	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       _UpperCamelCase			()-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # 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() == 1_00






