






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class       snake_case__ (				lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    )			->   Dict:






            '''simple docstring'''





            super().__init__()

            if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""     )

            self.register_modules(
                speech_model=a__      ,    speech_processor=a__      ,    vae=a__      ,    text_encoder=a__      ,    tokenizer=a__      ,    unet=a__      ,    scheduler=a__      ,    feature_extractor=a__      ,    )



       def        __lowercase    (				self      ,    a__ = "auto"     )			->   List[str]:






            '''simple docstring'''





            if slice_size == "auto":
                 __snake_case      :Optional[int] 							=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(a__     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            self.enable_attention_slicing(a__     )



       @torch.no_grad()
       def __call__(				self      ,    a__      ,    a__=1_60_00      ,    a__ = 5_12      ,    a__ = 5_12      ,    a__ = 50      ,    a__ = 7.5      ,    a__ = None      ,    a__ = 1      ,    a__ = 0.0      ,    a__ = None      ,    a__ = None      ,    a__ = "pil"      ,    a__ = True      ,    a__ = None      ,    a__ = 1      ,    **a__      ,    )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :str 							=    self.speech_processor.feature_extractor(
                a__      ,    return_tensors="""pt"""      ,    sampling_rate=a__     ).input_features.to(self.device     )
            __snake_case      :str 							=    self.speech_model.generate(a__      ,    max_length=48_00_00     )

            __snake_case      :Union[str, Any] 							=    self.speech_processor.tokenizer.batch_decode(a__      ,    skip_special_tokens=a__      ,    normalize=a__     )[
                0
            ]

            if isinstance(a__      ,    a__     ):
                 __snake_case      :Tuple 							=    1
            elif isinstance(a__      ,    a__     ):
                 __snake_case      :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
            __snake_case      :int 							=    self.tokenizer(
                a__      ,    padding="""max_length"""      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="""pt"""      ,    )
            __snake_case      :Optional[int] 							=    text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 __snake_case      :int 							=    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}'''     )
                 __snake_case      :Union[str, Any] 							=    text_input_ids[:, : self.tokenizer.model_max_length]
            __snake_case      :Any 							=    self.text_encoder(text_input_ids.to(self.device     )     )[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            __snake_case							,			__snake_case							,			__snake_case      :Optional[int] 							=    text_embeddings.shape
            __snake_case      :int 							=    text_embeddings.repeat(1      ,    a__      ,    1     )
            __snake_case      :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.
            __snake_case      :str 							=    guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 __snake_case      :List[str]
                 if negative_prompt is None:
                      __snake_case      :Dict 							=    [""""""] * batch_size
                 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__     ):
                      __snake_case      :int 							=    [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:
                      __snake_case      :Optional[Any] 							=    negative_prompt

                 __snake_case      :Dict 							=    text_input_ids.shape[-1]
                 __snake_case      :List[str] 							=    self.tokenizer(
                     a__      ,    padding="""max_length"""      ,    max_length=a__      ,    truncation=a__      ,    return_tensors="""pt"""      ,    )
                 __snake_case      :Any 							=    self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 __snake_case      :Optional[Any] 							=    uncond_embeddings.shape[1]
                 __snake_case      :int 							=    uncond_embeddings.repeat(1      ,    a__      ,    1     )
                 __snake_case      :Optional[Any] 							=    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
                 __snake_case      :List[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`.
            __snake_case      :str 							=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            __snake_case      :int 							=    text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      __snake_case      :Dict 							=    torch.randn(a__      ,    generator=a__      ,    device="""cpu"""      ,    dtype=a__     ).to(
                          self.device     )
                 else:
                      __snake_case      :List[Any] 							=    torch.randn(a__      ,    generator=a__      ,    device=self.device      ,    dtype=a__     )
            else:
                 if latents.shape != latents_shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
                 __snake_case      :List[Any] 							=    latents.to(self.device     )

            # 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
            __snake_case      :str 							=    self.scheduler.timesteps.to(self.device     )

            # scale the initial noise by the standard deviation required by the scheduler
            __snake_case      :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]
            __snake_case      :Any 							=    """eta""" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
            __snake_case      :Optional[int] 							=    {}
            if accepts_eta:
                 __snake_case      :Optional[int] 							=    eta

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

                 # predict the noise residual
                 __snake_case      :Union[str, Any] 							=    self.unet(a__      ,    a__      ,    encoder_hidden_states=a__     ).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 __snake_case      :Union[str, Any] 							=    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__     )

            __snake_case      :List[str] 							=    1 / 0.1_82_15 * latents
            __snake_case      :Union[str, Any] 							=    self.vae.decode(a__     ).sample

            __snake_case      :Tuple 							=    (image / 2 + 0.5).clamp(0      ,    1     )

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

            if output_type == "pil":
                 __snake_case      :Optional[Any] 							=    self.numpy_to_pil(a__     )

            if not return_dict:
                 return image

            return StableDiffusionPipelineOutput(images=a__      ,    nsfw_content_detected=a__     )










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	(							snake_case__  : List[Any]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    []
     for part_id in partition_order:
          __snake_case      :int 							=    df.where(f'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
          for row_idx, row in enumerate(snake_case__	):
               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	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Any 							=    spark.range(100	).repartition(1	)
     __snake_case      :int 							=    Spark(snake_case__	)
     # 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	(							):




     '''simple docstring'''






     __snake_case      :Tuple 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :int 							=    spark.range(10	).repartition(2	)
     __snake_case      :str 							=    [1, 0]
     __snake_case      :List[Any] 							=    _generate_iterable_examples(snake_case__						,snake_case__	)  # Reverse the partitions.
     __snake_case      :Union[str, Any] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,snake_case__	)

     for i, (row_id, row_dict) in enumerate(generate_fn()	):
          __snake_case							,			__snake_case      :Union[str, 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 	UpperCamelCase	(							):




     '''simple docstring'''






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






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Union[str, Any] 							=    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      :Dict 							=    lambda snake_case__	: x.reverse()
          __snake_case      :int 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[2, 1, 0]	)

          __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	).shuffle_data_sources(snake_case__	)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(snake_case__	):
               __snake_case							,			__snake_case      :List[str] 							=    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	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(20	).repartition(4	)

     # Partitions 0 and 2
     __snake_case      :List[Any] 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=0						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Dict 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[0, 2]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Tuple 							=    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      :str 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=1						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Optional[int] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[1, 3]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Dict 							=    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	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(100	).repartition(1	)
     __snake_case      :Dict 							=    Spark(snake_case__	)
     # 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


