





"""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


_UpperCamelCase							=      logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




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




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




				def 					__lowercase  (     self  :Any			):
											self.enable_attention_slicing(__lowercase			)




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

											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =1
											elif isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Optional[int]         =len(__lowercase			)
											else:
																		raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__lowercase			)}'			)

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

											# get prompt text embeddings
											__lowerCamelCase      :      str         =self.tokenizer(
											    __lowercase						,    padding='''max_length'''						,    max_length=self.tokenizer.model_max_length						,    return_tensors='''pt'''						,    )
											__lowerCamelCase      :      Dict         =text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		__lowerCamelCase      :      List[Any]         =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      :      Any         =text_input_ids[:, : self.tokenizer.model_max_length]

											if text_embeddings is None:
																		__lowerCamelCase      :      str         =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      :      Dict         =text_embeddings.shape
											__lowerCamelCase      :      Dict         =text_embeddings.repeat(1						,    __lowercase						,    1			)
											__lowerCamelCase      :      int         =text_embeddings.view(bs_embed * num_images_per_prompt						,    __lowercase						,    -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      :      Tuple         =guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																		__lowerCamelCase      :      List[str]
																		if negative_prompt is None:
																									__lowerCamelCase      :      Tuple         =['''''']
																		elif type(__lowercase			) is not type(__lowercase			):
																									raise TypeError(
																									    f'`negative_prompt` should be the same type to `prompt`, but got {type(__lowercase			)} !='
																									    f' {type(__lowercase			)}.'			)
																		elif isinstance(__lowercase						,    __lowercase			):
																									__lowerCamelCase      :      Optional[int]         =[negative_prompt]
																		elif batch_size != len(__lowercase			):
																									raise ValueError(
																									    f'`negative_prompt`: {negative_prompt} has batch size {len(__lowercase			)}, but `prompt`:'
																									    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																									    ''' the batch size of `prompt`.'''			)
																		else:
																									__lowerCamelCase      :      Tuple         =negative_prompt

																		__lowerCamelCase      :      Optional[int]         =text_input_ids.shape[-1]
																		__lowerCamelCase      :      List[Any]         =self.tokenizer(
																		    __lowercase						,    padding='''max_length'''						,    max_length=__lowercase						,    truncation=__lowercase						,    return_tensors='''pt'''						,    )
																		__lowerCamelCase      :      List[str]         =self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		__lowerCamelCase      :      Union[str, Any]         =uncond_embeddings.shape[1]
																		__lowerCamelCase      :      Dict         =uncond_embeddings.repeat(__lowercase						,    __lowercase						,    1			)
																		__lowerCamelCase      :      Dict         =uncond_embeddings.view(batch_size * num_images_per_prompt						,    __lowercase						,    -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      :      Dict         =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      :      Optional[int]         =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											__lowerCamelCase      :      str         =(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
											__lowerCamelCase      :      Tuple         =text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not exist on mps
																									__lowerCamelCase      :      Optional[Any]         =torch.randn(
																									    __lowercase						,    generator=__lowercase						,    device='''cpu'''						,    dtype=__lowercase			).to(self.device			)
																									__lowerCamelCase      :      Optional[Any]         =torch.randn(__lowercase						,    generator=__lowercase						,    device='''cpu'''						,    dtype=__lowercase			).to(
																									    self.device			)
																		else:
																									__lowerCamelCase      :      Optional[int]         =torch.randn(
																									    __lowercase						,    generator=__lowercase						,    device=self.device						,    dtype=__lowercase			)
																									__lowerCamelCase      :      Optional[Any]         =torch.randn(__lowercase						,    generator=__lowercase						,    device=self.device						,    dtype=__lowercase			)
											else:
																		if latents_reference.shape != latents_shape:
																									raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'			)
																		__lowerCamelCase      :      int         =latents_reference.to(self.device			)
																		__lowerCamelCase      :      Dict         =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      :      Optional[int]         =(latents_shape[3] - latents_shape_reference[3]) // 2
											__lowerCamelCase      :      int         =(latents_shape[2] - latents_shape_reference[2]) // 2
											__lowerCamelCase      :      List[Any]         =latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
											__lowerCamelCase      :      Any         =latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
											__lowerCamelCase      :      List[Any]         =0 if dx < 0 else dx
											__lowerCamelCase      :      Tuple         =0 if dy < 0 else dy
											__lowerCamelCase      :      int         =max(-dx						,    0			)
											__lowerCamelCase      :      str         =max(-dy						,    0			)
											# import pdb
											# pdb.set_trace()
											__lowerCamelCase      :      int         =latents_reference[:, :, dy : dy + h, dx : dx + w]

											# set timesteps
											self.scheduler.set_timesteps(__lowercase			)

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

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

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

																		# predict the noise residual
																		__lowerCamelCase      :      List[Any]         =self.unet(__lowercase						,    __lowercase						,    encoder_hidden_states=__lowercase			).sample

																		# perform guidance
																		if do_classifier_free_guidance:
																									__lowerCamelCase    ,						__lowerCamelCase      :      Union[str, Any]         =noise_pred.chunk(2			)
																									__lowerCamelCase      :      str         =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		__lowerCamelCase      :      List[str]         =self.scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    **__lowercase			).prev_sample

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

											__lowerCamelCase      :      Any         =1 / 0.18215 * latents
											__lowerCamelCase      :      Optional[Any]         =self.vae.decode(__lowercase			).sample

											__lowerCamelCase      :      Dict         =(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      :      Dict         =image.cpu().permute(0						,    2						,    3						,    1			).float().numpy()

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

											if output_type == "pil":
																		__lowerCamelCase      :      List[str]         =self.numpy_to_pil(__lowercase			)

											if not return_dict:
																		return (image, has_nsfw_concept)

											return StableDiffusionPipelineOutput(images=__lowercase						,    nsfw_content_detected=__lowercase			)











"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		LDMTextToImagePipeline
				__snake_case   :       Optional[Any]	 =		TEXT_TO_IMAGE_PARAMS - {
				    """negative_prompt""",
				    """negative_prompt_embeds""",
				    """cross_attention_kwargs""",
				    """prompt_embeds""",
				}
				__snake_case   :       str	 =		PipelineTesterMixin.required_optional_params - {
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				__snake_case   :       Union[str, Any]	 =		TEXT_TO_IMAGE_BATCH_PARAMS
				__snake_case   :       Optional[Any]	 =		False




				def 					__lowercase  (     self  :List[str]			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      str         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      str         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =AutoencoderKL(
											    block_out_channels=(32, 64)						,    in_channels=3						,    out_channels=3						,    down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')						,    up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')						,    latent_channels=4						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Any         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[int]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Dict         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vqvae''': vae,
											    '''bert''': text_encoder,
											    '''tokenizer''': tokenizer,
											}
											return components




				def 					__lowercase  (     self  :int						,    __lowercase  :Optional[int]						,    __lowercase  :Optional[Any]=0			):
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      str         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      List[str]         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      str         =self.get_dummy_components()
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline(**__lowercase			)
											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      str         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      List[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Optional[int]         =image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :int						,    __lowercase  :Any						,    __lowercase  :Optional[int]=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      List[str]         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      List[str]         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      List[str]         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      int         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Tuple         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Union[str, Any]         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											__lowerCamelCase      :      Union[str, Any]         =np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]			)
											__lowerCamelCase      :      Dict         =np.abs(expected_slice - image_slice			).max()
											assert max_diff < 1e-3

@nightly
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Optional[Any]						,    __lowercase  :int=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      str         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[int]         =pipe(**__lowercase			).images[0]

											__lowerCamelCase      :      Optional[int]         =load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''			)
											__lowerCamelCase      :      Dict         =np.abs(expected_image - image			).max()
											assert max_diff < 1e-3




