





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


_snake_case  :    List[str]						    =  logging.get_logger(__name__)  # pylint: disable=invalid-name



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def __init__(				self :		Optional[int]					,  lowerCAmelCase_ :		AutoencoderKL					,  lowerCAmelCase_ :		CLIPTextModel					,  lowerCAmelCase_ :		CLIPTokenizer					,  lowerCAmelCase_ :		UNetaDConditionModel					,  lowerCAmelCase_ :		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,  lowerCAmelCase_ :		StableDiffusionSafetyChecker					,  lowerCAmelCase_ :		CLIPImageProcessor					,  )							->						str:
     super().__init__()
     self.register_modules(
         vae=lowerCAmelCase_					,  text_encoder=lowerCAmelCase_					,  tokenizer=lowerCAmelCase_					,  unet=lowerCAmelCase_					,  scheduler=lowerCAmelCase_					,  safety_checker=lowerCAmelCase_					,  feature_extractor=lowerCAmelCase_					,  )






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






  def 				lowercase		(				self :		Tuple     )							->						Any:
     self.enable_attention_slicing(lowerCAmelCase_     )






  @torch.no_grad()
  def __call__(				self :		Tuple					,  lowerCAmelCase_ :		Union[str, List[str]]					,  lowerCAmelCase_ :		int = 5_1_2					,  lowerCAmelCase_ :		int = 5_1_2					,  lowerCAmelCase_ :		int = 5_0					,  lowerCAmelCase_ :		float = 7.5					,  lowerCAmelCase_ :		Optional[Union[str, List[str]]] = None					,  lowerCAmelCase_ :		Optional[int] = 1					,  lowerCAmelCase_ :		float = 0.0					,  lowerCAmelCase_ :		Optional[torch.Generator] = None					,  lowerCAmelCase_ :		Optional[torch.FloatTensor] = None					,  lowerCAmelCase_ :		Optional[str] = "pil"					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,  lowerCAmelCase_ :		int = 1					,  lowerCAmelCase_ :		Optional[torch.FloatTensor] = None					,  **lowerCAmelCase_ :		Optional[Any]					,  )							->						Any:

     if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	1
     elif isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	len(lowerCAmelCase_     )
     else:
        raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_     )}"""     )

     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(lowerCAmelCase_					,  lowerCAmelCase_     ) or callback_steps <= 0)
     ):
        raise ValueError(
            f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            f""" {type(lowerCAmelCase_     )}."""     )

     # get prompt text embeddings
     __lowerCAmelCase								=	self.tokenizer(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=self.tokenizer.model_max_length					,  return_tensors='pt'					,  )
     __lowerCAmelCase								=	text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        __lowerCAmelCase								=	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}"""     )
        __lowerCAmelCase								=	text_input_ids[:, : self.tokenizer.model_max_length]

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

     # duplicate text embeddings for each generation per prompt, using mps friendly method
     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	text_embeddings.shape
     __lowerCAmelCase								=	text_embeddings.repeat(1					,  lowerCAmelCase_					,  1     )
     __lowerCAmelCase								=	text_embeddings.view(bs_embed * num_images_per_prompt					,  lowerCAmelCase_					,  -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.
     __lowerCAmelCase								=	guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
        __lowerCAmelCase								=	42
        if negative_prompt is None:
           __lowerCAmelCase								=	['']
        elif type(lowerCAmelCase_     ) is not type(lowerCAmelCase_     ):
           raise TypeError(
               f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_     )} !="""
               f""" {type(lowerCAmelCase_     )}."""     )
        elif isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ):
           __lowerCAmelCase								=	[negative_prompt]
        elif batch_size != len(lowerCAmelCase_     ):
           raise ValueError(
               f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_     )}, but `prompt`:"""
               f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
               ' the batch size of `prompt`.'     )
        else:
           __lowerCAmelCase								=	negative_prompt

        __lowerCAmelCase								=	text_input_ids.shape[-1]
        __lowerCAmelCase								=	self.tokenizer(
            lowerCAmelCase_					,  padding='max_length'					,  max_length=lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  return_tensors='pt'					,  )
        __lowerCAmelCase								=	self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        __lowerCAmelCase								=	uncond_embeddings.shape[1]
        __lowerCAmelCase								=	uncond_embeddings.repeat(lowerCAmelCase_					,  lowerCAmelCase_					,  1     )
        __lowerCAmelCase								=	uncond_embeddings.view(batch_size * num_images_per_prompt					,  lowerCAmelCase_					,  -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
        __lowerCAmelCase								=	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`.
     __lowerCAmelCase								=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     __lowerCAmelCase								=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
     __lowerCAmelCase								=	text_embeddings.dtype
     if latents is None:
        if self.device.type == "mps":
           # randn does not exist on mps
           __lowerCAmelCase								=	torch.randn(
               lowerCAmelCase_					,  generator=lowerCAmelCase_					,  device='cpu'					,  dtype=lowerCAmelCase_     ).to(self.device     )
           __lowerCAmelCase								=	torch.randn(lowerCAmelCase_					,  generator=lowerCAmelCase_					,  device='cpu'					,  dtype=lowerCAmelCase_     ).to(
               self.device     )
        else:
           __lowerCAmelCase								=	torch.randn(
               lowerCAmelCase_					,  generator=lowerCAmelCase_					,  device=self.device					,  dtype=lowerCAmelCase_     )
           __lowerCAmelCase								=	torch.randn(lowerCAmelCase_					,  generator=lowerCAmelCase_					,  device=self.device					,  dtype=lowerCAmelCase_     )
     else:
        if latents_reference.shape != latents_shape:
           raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""     )
        __lowerCAmelCase								=	latents_reference.to(self.device     )
        __lowerCAmelCase								=	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
     __lowerCAmelCase								=	(latents_shape[3] - latents_shape_reference[3]) // 2
     __lowerCAmelCase								=	(latents_shape[2] - latents_shape_reference[2]) // 2
     __lowerCAmelCase								=	latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
     __lowerCAmelCase								=	latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
     __lowerCAmelCase								=	0 if dx < 0 else dx
     __lowerCAmelCase								=	0 if dy < 0 else dy
     __lowerCAmelCase								=	max(-dx					,  0     )
     __lowerCAmelCase								=	max(-dy					,  0     )
     # import pdb
     # pdb.set_trace()
     __lowerCAmelCase								=	latents_reference[:, :, dy : dy + h, dx : dx + w]

     # set timesteps
     self.scheduler.set_timesteps(lowerCAmelCase_     )

     # Some schedulers like PNDM have timesteps as arrays
     # It's more optimized to move all timesteps to correct device beforehand
     __lowerCAmelCase								=	self.scheduler.timesteps.to(self.device     )

     # scale the initial noise by the standard deviation required by the scheduler
     __lowerCAmelCase								=	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]
     __lowerCAmelCase								=	'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
     __lowerCAmelCase								=	{}
     if accepts_eta:
        __lowerCAmelCase								=	eta

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

        # predict the noise residual
        __lowerCAmelCase								=	self.unet(lowerCAmelCase_					,  lowerCAmelCase_					,  encoder_hidden_states=lowerCAmelCase_     ).sample

        # perform guidance
        if do_classifier_free_guidance:
           __lowerCAmelCase	,	__lowerCAmelCase								=	noise_pred.chunk(2     )
           __lowerCAmelCase								=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        __lowerCAmelCase								=	self.scheduler.step(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  **lowerCAmelCase_     ).prev_sample

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

     __lowerCAmelCase								=	1 / 0.1_82_15 * latents
     __lowerCAmelCase								=	self.vae.decode(lowerCAmelCase_     ).sample

     __lowerCAmelCase								=	(image / 2 + 0.5).clamp(0					,  1     )

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

     if self.safety_checker is not None:
        __lowerCAmelCase								=	self.feature_extractor(self.numpy_to_pil(lowerCAmelCase_     )					,  return_tensors='pt'     ).to(
            self.device     )
        __lowerCAmelCase	,	__lowerCAmelCase								=	self.safety_checker(
            images=lowerCAmelCase_					,  clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype     )     )
     else:
        __lowerCAmelCase								=	None

     if output_type == "pil":
        __lowerCAmelCase								=	self.numpy_to_pil(lowerCAmelCase_     )

     if not return_dict:
        return (image, has_nsfw_concept)

     return StableDiffusionPipelineOutput(images=lowerCAmelCase_					,  nsfw_content_detected=lowerCAmelCase_     )








import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs

@pytest.mark.parametrize(
    'kwargs, expected',	[
        ({'num_shards': 0, 'max_num_jobs': 1}, []),
        ({'num_shards': 10, 'max_num_jobs': 1}, [range(10	)]),
        ({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_,	i + 1	) for i in range(10	)]),
        ({'num_shards': 1, 'max_num_jobs': 10}, [range(1	)]),
        ({'num_shards': 10, 'max_num_jobs': 3}, [range(0,	4	), range(4,	7	), range(7,	10	)]),
        ({'num_shards': 3, 'max_num_jobs': 10}, [range(0,	1	), range(1,	2	), range(2,	3	)]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	_distribute_shards(**lowerCAmelCase_	)
   assert out == expected

@pytest.mark.parametrize(
    'gen_kwargs, max_num_jobs, expected',	[
        ({'foo': 0}, 10, [{'foo': 0}]),
        ({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
        ({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
        ({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
        ({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	_split_gen_kwargs(lowerCAmelCase_,	lowerCAmelCase_	)
   assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, expected',	[
        ({'foo': 0}, 1),
        ({'shards': [0]}, 1),
        ({'shards': [0, 1, 2, 3]}, 4),
        ({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
        ({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
        ({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
    ],	)
def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   if expected is RuntimeError:
      with pytest.raises(lowerCAmelCase_	):
         _number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
   else:
      __lowerCAmelCase								=	_number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
      assert out == expected

