






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					DiTPipeline
     snake_case__					=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     snake_case__					=					PipelineTesterMixin.required_optional_params - {
         """latents""",
         """num_images_per_prompt""",
         """callback""",
         """callback_steps""",
     }
     snake_case__					=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     snake_case__					=					False







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  TransformeraDModel(
               sample_size=16   ,						num_layers=2   ,						patch_size=4   ,						attention_head_dim=8   ,						num_attention_heads=2   ,						in_channels=4   ,						out_channels=8   ,						attention_bias=_SCREAMING_SNAKE_CASE   ,						activation_fn='gelu-approximate'   ,						num_embeds_ada_norm=1000   ,						norm_type='ada_norm_zero'   ,						norm_elementwise_affine=_SCREAMING_SNAKE_CASE   ,						)
           UpperCamelCase					       =  AutoencoderKL()
           UpperCamelCase					       =  DDIMScheduler()
           UpperCamelCase					       =  {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
           return components







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=0 ):






           """simple docstring"""




           if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
                 UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           else:
                 UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  {
               'class_labels': [1],
               'generator': generator,
               'num_inference_steps': 2,
               'output_type': 'numpy',
           }
           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'

           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  self.pipeline_class(**_SCREAMING_SNAKE_CASE )
           pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  pipe(**_SCREAMING_SNAKE_CASE ).images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]

           self.assertEqual(image.shape   ,						(1, 16, 16, 3) )
           UpperCamelCase					       =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7] )
           UpperCamelCase					       =  np.abs(image_slice.flatten() - expected_slice ).max()
           self.assertLessEqual(_SCREAMING_SNAKE_CASE   ,						1E-3 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE   ,						expected_max_diff=1E-3 )







     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()   ,						reason='XFormers attention is only available with CUDA and `xformers` installed'   ,						)
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





@require_torch_gpu
@slow
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  torch.manual_seed(0 )

           UpperCamelCase					       =  DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
           pipe.to('cuda' )

           UpperCamelCase					       =  ['vase', 'umbrella', 'white shark', 'white wolf']
           UpperCamelCase					       =  pipe.get_label_ids(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  pipe(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=40   ,						output_type='np' ).images

           for word, image in zip(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  load_numpy(
                     f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
                 assert np.abs((expected_image - image).max() ) < 1E-2







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
           UpperCamelCase					       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
           pipe.to('cuda' )

           UpperCamelCase					       =  ['vase', 'umbrella']
           UpperCamelCase					       =  pipe.get_label_ids(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  torch.manual_seed(0 )
           UpperCamelCase					       =  pipe(_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						num_inference_steps=25   ,						output_type='np' ).images

           for word, image in zip(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  load_numpy(
                     'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                     f'/dit/{word}_512.npy' )

                 assert np.abs((expected_image - image).max() ) < 1E-1











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


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





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    WhisperForConditionalGeneration   ,						_SCREAMING_SNAKE_CASE      :    WhisperProcessor   ,						_SCREAMING_SNAKE_CASE      :    AutoencoderKL   ,						_SCREAMING_SNAKE_CASE      :    CLIPTextModel   ,						_SCREAMING_SNAKE_CASE      :    CLIPTokenizer   ,						_SCREAMING_SNAKE_CASE      :    UNetaDConditionModel   ,						_SCREAMING_SNAKE_CASE      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,						_SCREAMING_SNAKE_CASE      :    StableDiffusionSafetyChecker   ,						_SCREAMING_SNAKE_CASE      :    CLIPImageProcessor   ,						):






           """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=_SCREAMING_SNAKE_CASE   ,						speech_processor=_SCREAMING_SNAKE_CASE   ,						vae=_SCREAMING_SNAKE_CASE   ,						text_encoder=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						unet=_SCREAMING_SNAKE_CASE   ,						scheduler=_SCREAMING_SNAKE_CASE   ,						feature_extractor=_SCREAMING_SNAKE_CASE   ,						)







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, int]] = "auto" ):






           """simple docstring"""




           if slice_size == "auto":
                 UpperCamelCase					       =  self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )







     @torch.no_grad()
     def __call__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=1_6000   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 512   ,						_SCREAMING_SNAKE_CASE      :    int = 50   ,						_SCREAMING_SNAKE_CASE      :    float = 7.5   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, List[str]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = 1   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.Generator] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[torch.FloatTensor] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = "pil"   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  self.speech_processor.feature_extractor(
               _SCREAMING_SNAKE_CASE   ,						return_tensors='pt'   ,						sampling_rate=_SCREAMING_SNAKE_CASE ).input_features.to(self.device )
           UpperCamelCase					       =  self.speech_model.generate(_SCREAMING_SNAKE_CASE   ,						max_length=48_0000 )

           UpperCamelCase					       =  self.speech_processor.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE   ,						normalize=_SCREAMING_SNAKE_CASE )[
               0
           ]

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

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

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

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 UpperCamelCase					       =  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}' )
                 UpperCamelCase					       =  text_input_ids[:, : self.tokenizer.model_max_length]
           UpperCamelCase					       =  self.text_encoder(text_input_ids.to(self.device ) )[0]

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

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

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

           # set timesteps
           self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )

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

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

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

                 # predict the noise residual
                 UpperCamelCase					       =  self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE ).sample

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

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

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

           UpperCamelCase					       =  1 / 0.1_8_2_1_5 * latents
           UpperCamelCase					       =  self.vae.decode(_SCREAMING_SNAKE_CASE ).sample

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

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

           if output_type == "pil":
                 UpperCamelCase					       =  self.numpy_to_pil(_SCREAMING_SNAKE_CASE )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE   ,						nsfw_content_detected=_SCREAMING_SNAKE_CASE )



