


'''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 	_A  (       A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]
	for part_id in partition_order:
		lowercase						:		Optional[Any]   					=		df.where(F'''SPARK_PARTITION_ID() = {part_id}'''							).collect()
		for row_idx, row in enumerate(A							):
			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 	_A  (       )  ->				Optional[Any]:
	lowercase						:		str   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		List[str]   					=		spark.range(1_0_0							).repartition(1							)
	lowercase						:		Union[str, Any]   					=		Spark(A							)
	# 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=1_6							)
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 5_0




@require_not_windows
@require_dill_gt_0_3_2
def 	_A  (       )  ->				int:
	lowercase						:		List[Any]   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		Optional[int]   					=		spark.range(1_0							).repartition(2							)
	lowercase						:		Dict   					=		[1, 0]
	lowercase						:		Union[str, Any]   					=		_generate_iterable_examples(A							,A							)  # Reverse the partitions.
	lowercase						:		List[Any]   					=		_get_expected_row_ids_and_row_dicts_for_partition_order(A							,A							)

	for i, (row_id, row_dict) in enumerate(generate_fn()							):
		lowercase					,		lowercase						:		Any   					=		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 	_A  (       )  ->				str:
	lowercase						:		str   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		Optional[int]   					=		spark.range(1_0							).repartition(1							)
	lowercase						:		Union[str, Any]   					=		SparkExamplesIterable(A							)
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(A							):
		assert row_id == F'''0_{i}'''
		assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def 	_A  (       )  ->				str:
	lowercase						:		Any   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		List[Any]   					=		spark.range(3_0							).repartition(3							)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("numpy.random.Generator"							) as generator_mock:
		lowercase						:		Any   					=		lambda A							: x.reverse()
		lowercase						:		Tuple   					=		_get_expected_row_ids_and_row_dicts_for_partition_order(A							,[2, 1, 0]							)

		lowercase						:		Any   					=		SparkExamplesIterable(A							).shuffle_data_sources(A							)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(A							):
			lowercase					,		lowercase						:		Optional[Any]   					=		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 	_A  (       )  ->				int:
	lowercase						:		List[str]   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		int   					=		spark.range(2_0							).repartition(4							)

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

	# Partitions 1 and 3
	lowercase						:		Any   					=		SparkExamplesIterable(A							).shard_data_sources(worker_id=1							,num_workers=2							)
	assert shard_it_a.n_shards == 2
	lowercase						:		Any   					=		_get_expected_row_ids_and_row_dicts_for_partition_order(A							,[1, 3]							)
	for i, (row_id, row_dict) in enumerate(A							):
		lowercase					,		lowercase						:		str   					=		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 	_A  (       )  ->				Tuple:
	lowercase						:		Optional[Any]   					=		pyspark.sql.SparkSession.builder.master("local[*]"							).appName("pyspark"							).getOrCreate()
	lowercase						:		List[str]   					=		spark.range(1_0_0							).repartition(1							)
	lowercase						:		Optional[int]   					=		Spark(A							)
	# 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_0_0






'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowerCAmelCase   : Any             =				logging.get_logger(__name__)  # pylint: disable=invalid-name




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Union[str, Any]:
					super().__init__()
					self.register_modules(
					    vae=a_							,      text_encoder=a_							,      tokenizer=a_							,      unet=a_							,      scheduler=a_							,      safety_checker=a_							,      feature_extractor=a_							,      )

				def 	a__		(     self							,      a_ = "auto"							)    ->  Dict:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						lowercase						:		Union[str, Any]   					=		self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(a_							)

				def 	a__		(     self							)    ->  Dict:
					self.enable_attention_slicing(a_							)


				@torch.no_grad()
				def __call__(     self							,      a_							,      a_ = 5_1_2							,      a_ = 5_1_2							,      a_ = 5_0							,      a_ = 7.5							,      a_ = None							,      a_ = 1							,      a_ = 0.0							,      a_ = None							,      a_ = None							,      a_ = "pil"							,      a_ = True							,      a_ = None							,      a_ = 1							,      a_ = None							,      **a_							,      )    ->  Union[str, Any]:

					if isinstance(a_							,      a_							):
						lowercase						:		Tuple   					=		1
					elif isinstance(a_							,      a_							):
						lowercase						:		int   					=		len(a_							)
					else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a_							)}'''							)

					if height % 8 != 0 or width % 8 != 0:
						raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(a_							,      a_							) or callback_steps <= 0)
					):
						raise ValueError(
						    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
						    F''' {type(a_							)}.'''							)

					# get prompt text embeddings
					lowercase						:		int   					=		self.tokenizer(
					    a_							,      padding="max_length"							,      max_length=self.tokenizer.model_max_length							,      return_tensors="pt"							,      )
					lowercase						:		List[Any]   					=		text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						lowercase						:		str   					=		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
						logger.warning(
						    "The following part of your input was truncated because CLIP can only handle sequences up to"
						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
						lowercase						:		Dict   					=		text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
						lowercase						:		List[Any]   					=		self.text_encoder(text_input_ids.to(self.device							)							)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					lowercase					,		lowercase					,		lowercase						:		List[Any]   					=		text_embeddings.shape
					lowercase						:		Tuple   					=		text_embeddings.repeat(1							,      a_							,      1							)
					lowercase						:		Optional[int]   					=		text_embeddings.view(bs_embed * num_images_per_prompt							,      a_							,      -1							)

					# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
					# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
					# corresponds to doing no classifier free guidance.
					lowercase						:		str   					=		guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
						lowercase						:		List[str]
						if negative_prompt is None:
							lowercase						:		Optional[Any]   					=		[""]
						elif type(a_							) is not type(a_							):
							raise TypeError(
							    F'''`negative_prompt` should be the same type to `prompt`, but got {type(a_							)} !='''
							    F''' {type(a_							)}.'''							)
						elif isinstance(a_							,      a_							):
							lowercase						:		Tuple   					=		[negative_prompt]
						elif batch_size != len(a_							):
							raise ValueError(
							    F'''`negative_prompt`: {negative_prompt} has batch size {len(a_							)}, but `prompt`:'''
							    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
							    " the batch size of `prompt`."							)
						else:
							lowercase						:		Dict   					=		negative_prompt

						lowercase						:		Tuple   					=		text_input_ids.shape[-1]
						lowercase						:		Any   					=		self.tokenizer(
						    a_							,      padding="max_length"							,      max_length=a_							,      truncation=a_							,      return_tensors="pt"							,      )
						lowercase						:		Any   					=		self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						lowercase						:		Union[str, Any]   					=		uncond_embeddings.shape[1]
						lowercase						:		Tuple   					=		uncond_embeddings.repeat(a_							,      a_							,      1							)
						lowercase						:		Optional[int]   					=		uncond_embeddings.view(batch_size * num_images_per_prompt							,      a_							,      -1							)

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						lowercase						:		Optional[Any]   					=		torch.cat([uncond_embeddings, text_embeddings]							)

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					lowercase						:		List[str]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					lowercase						:		Union[str, Any]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
					lowercase						:		Union[str, Any]   					=		text_embeddings.dtype
					if latents is None:
						if self.device.type == "mps":
							# randn does not exist on mps
							lowercase						:		List[str]   					=		torch.randn(
							    a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(self.device							)
							lowercase						:		Any   					=		torch.randn(a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(
							    self.device							)
						else:
							lowercase						:		List[Any]   					=		torch.randn(
							    a_							,      generator=a_							,      device=self.device							,      dtype=a_							)
							lowercase						:		Optional[Any]   					=		torch.randn(a_							,      generator=a_							,      device=self.device							,      dtype=a_							)
					else:
						if latents_reference.shape != latents_shape:
							raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
						lowercase						:		Dict   					=		latents_reference.to(self.device							)
						lowercase						:		int   					=		latents.to(self.device							)

					# This is the key part of the pipeline where we
					# try to ensure that the generated images w/ the same seed
					# but different sizes actually result in similar images
					lowercase						:		int   					=		(latents_shape[3] - latents_shape_reference[3]) // 2
					lowercase						:		Union[str, Any]   					=		(latents_shape[2] - latents_shape_reference[2]) // 2
					lowercase						:		int   					=		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
					lowercase						:		Dict   					=		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
					lowercase						:		Optional[int]   					=		0 if dx < 0 else dx
					lowercase						:		Tuple   					=		0 if dy < 0 else dy
					lowercase						:		Optional[int]   					=		max(-dx							,      0							)
					lowercase						:		int   					=		max(-dy							,      0							)
					# import pdb
					# pdb.set_trace()
					lowercase						:		Union[str, Any]   					=		latents_reference[:, :, dy : dy + h, dx : dx + w]

					# set timesteps
					self.scheduler.set_timesteps(a_							)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					lowercase						:		List[Any]   					=		self.scheduler.timesteps.to(self.device							)

					# scale the initial noise by the standard deviation required by the scheduler
					lowercase						:		List[str]   					=		latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					lowercase						:		Optional[Any]   					=		"eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
					lowercase						:		List[str]   					=		{}
					if accepts_eta:
						lowercase						:		List[str]   					=		eta

					for i, t in enumerate(self.progress_bar(a_							)							):
						# expand the latents if we are doing classifier free guidance
						lowercase						:		Optional[int]   					=		torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
						lowercase						:		int   					=		self.scheduler.scale_model_input(a_							,      a_							)

						# predict the noise residual
						lowercase						:		Dict   					=		self.unet(a_							,      a_							,      encoder_hidden_states=a_							).sample

						# perform guidance
						if do_classifier_free_guidance:
							lowercase					,		lowercase						:		Dict   					=		noise_pred.chunk(2							)
							lowercase						:		Optional[int]   					=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# compute the previous noisy sample x_t -> x_t-1
						lowercase						:		int   					=		self.scheduler.step(a_							,      a_							,      a_							,      **a_							).prev_sample

						# call the callback, if provided
						if callback is not None and i % callback_steps == 0:
							callback(a_							,      a_							,      a_							)

					lowercase						:		List[Any]   					=		1 / 0.1_82_15 * latents
					lowercase						:		Tuple   					=		self.vae.decode(a_							).sample

					lowercase						:		Any   					=		(image / 2 + 0.5).clamp(0							,      1							)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					lowercase						:		Any   					=		image.cpu().permute(0							,      2							,      3							,      1							).float().numpy()

					if self.safety_checker is not None:
						lowercase						:		Tuple   					=		self.feature_extractor(self.numpy_to_pil(a_							)							,      return_tensors="pt"							).to(
						    self.device							)
						lowercase					,		lowercase						:		Tuple   					=		self.safety_checker(
						    images=a_							,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
					else:
						lowercase						:		Union[str, Any]   					=		None

					if output_type == "pil":
						lowercase						:		Union[str, Any]   					=		self.numpy_to_pil(a_							)

					if not return_dict:
						return (image, has_nsfw_concept)

					return StableDiffusionPipelineOutput(images=a_							,      nsfw_content_detected=a_							)


