





'''simple docstring'''






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


A :			Any							   =      getLogger(__name__)


A :			Dict							   =      4
A :			Optional[int]							   =      7

A :			List[Any]							   =      {
    'float16': torch.floataa,
    'float32': torch.floataa,
}





def    lowercase_   (		lowercase__    ) ->Optional[Any]:
						if isinstance(lowercase__					,	torch.Tensor    ):
												return image
						elif isinstance(lowercase__					,	PIL.Image.Image    ):
												_snake_case: List[str]	       =		[image]

						if isinstance(image[0]					,	PIL.Image.Image    ):
												_snake_case       ,    _snake_case: Union[str, Any]	       =		image[0].size
												_snake_case       ,    _snake_case: int	       =		(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												_snake_case: Dict	       =		[np.array(i.resize((w, h)    )    )[None, :] for i in image]
												_snake_case: List[str]	       =		np.concatenate(lowercase__					,	axis=0    )
												_snake_case: Optional[int]	       =		np.array(lowercase__    ).astype(np.floataa    ) / 2_5_5.0
												_snake_case: str	       =		image.transpose(0					,	3					,	1					,	2    )
												_snake_case: Optional[int]	       =		2.0 * image - 1.0
												_snake_case: Tuple	       =		torch.from_numpy(lowercase__    )
						elif isinstance(image[0]					,	torch.Tensor    ):
												_snake_case: List[Any]	       =		torch.cat(lowercase__					,	dim=0    )

						return image







class       lowerCamelCase       (    __UpperCAmelCase	):


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	OnnxRuntimeModel							,  __snake_case						:	OnnxRuntimeModel							,  __snake_case						:	Any							,  __snake_case						:	OnnxRuntimeModel							,  __snake_case						:	DDPMScheduler							,  __snake_case						:	Any							,  __snake_case						:	int = 3_50							,  ):





											'''simple docstring'''





											super().__init__(
											    vae=__snake_case							,  text_encoder=__snake_case							,  tokenizer=__snake_case							,  unet=__snake_case							,  low_res_scheduler=__snake_case							,  scheduler=__snake_case							,  safety_checker=__snake_case							,  feature_extractor=__snake_case							,  watermarker=__snake_case							,  max_noise_level=__snake_case							,  )


					def __call__(       self						:	List[str]							,  __snake_case						:	Union[str, List[str]]							,  __snake_case						:	Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]							,  __snake_case						:	int = 75							,  __snake_case						:	float = 9.0							,  __snake_case						:	int = 20							,  __snake_case						:	Optional[Union[str, List[str]]] = None							,  __snake_case						:	Optional[int] = 1							,  __snake_case						:	float = 0.0							,  __snake_case						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,  __snake_case						:	Optional[torch.FloatTensor] = None							,  __snake_case						:	Optional[np.ndarray] = None							,  __snake_case						:	Optional[np.ndarray] = None							,  __snake_case						:	Optional[str] = "pil"							,  __snake_case						:	bool = True							,  __snake_case						:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,  __snake_case						:	Optional[int] = 1							,  ):





											'''simple docstring'''





											self.check_inputs(__snake_case							,  __snake_case							,  __snake_case							,  __snake_case							)

											# 2. Define call parameters
											if prompt is not None and isinstance(__snake_case							,  __snake_case							):
																	_snake_case: Union[str, Any]	       =		1
											elif prompt is not None and isinstance(__snake_case							,  __snake_case							):
																	_snake_case: Tuple	       =		len(__snake_case							)
											else:
																	_snake_case: List[Any]	       =		prompt_embeds.shape[0]

											_snake_case: Any	       =		self._execution_device
											# 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: List[Any]	       =		guidance_scale > 1.0

											# 3. Encode input prompt
											_snake_case: Dict	       =		self._encode_prompt(
											    __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  prompt_embeds=__snake_case							,  negative_prompt_embeds=__snake_case							,  )

											_snake_case: Any	       =		ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

											# 4. Preprocess image
											_snake_case: str	       =		preprocess(__snake_case							)
											_snake_case: Any	       =		image.cpu()

											# 5. set timesteps
											self.scheduler.set_timesteps(__snake_case							,  device=__snake_case							)
											_snake_case: Optional[int]	       =		self.scheduler.timesteps

											# 5. Add noise to image
											_snake_case: Optional[Any]	       =		torch.tensor([noise_level]							,  dtype=torch.long							,  device=__snake_case							)
											_snake_case: Dict	       =		torch.randn(image.shape							,  generator=__snake_case							,  device=__snake_case							,  dtype=__snake_case							)
											_snake_case: Dict	       =		self.low_res_scheduler.add_noise(__snake_case							,  __snake_case							,  __snake_case							)

											_snake_case: Dict	       =		2 if do_classifier_free_guidance else 1
											_snake_case: Dict	       =		np.concatenate([image] * batch_multiplier * num_images_per_prompt							)
											_snake_case: List[str]	       =		np.concatenate([noise_level] * image.shape[0]							)

											# 6. Prepare latent variables
											_snake_case       ,    _snake_case: List[Any]	       =		image.shape[2:]
											_snake_case: Tuple	       =		self.prepare_latents(
											    batch_size * num_images_per_prompt							,  __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  __snake_case							,  )

											# 7. Check that sizes of image and latents match
											_snake_case: Tuple	       =		image.shape[1]
											if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																	raise ValueError(
																	    'Incorrect configuration settings! The config of `pipeline.unet` expects'
																	    f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
																	    f''' `num_channels_image`: {num_channels_image} '''
																	    f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
																	    ' `pipeline.unet` or your `image` input.'							)

											# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
											_snake_case: List[str]	       =		self.prepare_extra_step_kwargs(__snake_case							,  __snake_case							)

											_snake_case: Tuple	       =		next(
											    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')							,  'tensor(float)'							)
											_snake_case: Any	       =		ORT_TO_NP_TYPE[timestep_dtype]

											# 9. Denoising loop
											_snake_case: Optional[Any]	       =		len(__snake_case							) - num_inference_steps * self.scheduler.order
											with self.progress_bar(total=__snake_case							) as progress_bar:
																	for i, t in enumerate(__snake_case							):
																							# expand the latents if we are doing classifier free guidance
																							_snake_case: Any	       =		np.concatenate([latents] * 2							) if do_classifier_free_guidance else latents

																							# concat latents, mask, masked_image_latents in the channel dimension
																							_snake_case: int	       =		self.scheduler.scale_model_input(__snake_case							,  __snake_case							)
																							_snake_case: str	       =		np.concatenate([latent_model_input, image]							,  axis=1							)

																							# timestep to tensor
																							_snake_case: Dict	       =		np.array([t]							,  dtype=__snake_case							)

																							# predict the noise residual
																							_snake_case: Tuple	       =		self.unet(
																							    sample=__snake_case							,  timestep=__snake_case							,  encoder_hidden_states=__snake_case							,  class_labels=noise_level.astype(np.intaa							)							,  )[0]

																							# perform guidance
																							if do_classifier_free_guidance:
																													_snake_case       ,    _snake_case: List[Any]	       =		np.split(__snake_case							,  2							)
																							_snake_case: List[str]	       =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																							# compute the previous noisy sample x_t -> x_t-1
																							_snake_case: Tuple	       =		self.scheduler.step(
																							    torch.from_numpy(__snake_case							)							,  __snake_case							,  __snake_case							,  **__snake_case							).prev_sample

																							# call the callback, if provided
																							if i == len(__snake_case							) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																													progress_bar.update()
																													if callback is not None and i % callback_steps == 0:
																																			callback(__snake_case							,  __snake_case							,  __snake_case							)

        # 10. Post-processing
											_snake_case: int	       =		self.decode_latents(latents.float()							)

											# 11. Convert to PIL
											if output_type == "pil":
																	_snake_case: Any	       =		self.numpy_to_pil(__snake_case							)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		1 / 0.08_333 * latents
											_snake_case: Optional[int]	       =		self.vae(latent_sample=__snake_case							)[0]
											_snake_case: Union[str, Any]	       =		np.clip(image / 2 + 0.5							,  0							,  1							)
											_snake_case: Union[str, Any]	       =		image.transpose((0, 2, 3, 1)							)
											return image




					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Union[str, List[str]]							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[int]							,  __snake_case						:	bool							,  __snake_case						:	Optional[str]							,  __snake_case						:	Optional[torch.FloatTensor] = None							,  __snake_case						:	Optional[torch.FloatTensor] = None							,  ):





											'''simple docstring'''





											if prompt is not None and isinstance(__snake_case							,  __snake_case							):
																	_snake_case: int	       =		1
											elif prompt is not None and isinstance(__snake_case							,  __snake_case							):
																	_snake_case: Union[str, Any]	       =		len(__snake_case							)
											else:
																	_snake_case: Dict	       =		prompt_embeds.shape[0]

											if prompt_embeds is None:
																	_snake_case: Dict	       =		self.tokenizer(
																	    __snake_case							,  padding='max_length'							,  max_length=self.tokenizer.model_max_length							,  truncation=__snake_case							,  return_tensors='pt'							,  )
																	_snake_case: List[str]	       =		text_inputs.input_ids
																	_snake_case: List[str]	       =		self.tokenizer(__snake_case							,  padding='longest'							,  return_tensors='pt'							).input_ids

																	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																	    __snake_case							,  __snake_case							):
																							_snake_case: Union[str, Any]	       =		self.tokenizer.batch_decode(
																							    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
																							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}'''							)

																	# no positional arguments to text_encoder
																	_snake_case: List[str]	       =		self.text_encoder(
																	    input_ids=text_input_ids.int().to(__snake_case							)							,  )
																	_snake_case: Any	       =		prompt_embeds[0]

											_snake_case       ,    _snake_case       ,    _snake_case: str	       =		prompt_embeds.shape
											# duplicate text embeddings for each generation per prompt, using mps friendly method
											_snake_case: Union[str, Any]	       =		prompt_embeds.repeat(1							,  __snake_case							)
											_snake_case: List[str]	       =		prompt_embeds.reshape(bs_embed * num_images_per_prompt							,  __snake_case							,  -1							)

											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance and negative_prompt_embeds is None:
																	_snake_case: List[str]
																	if negative_prompt is None:
																							_snake_case: List[str]	       =		[''] * batch_size
																	elif type(__snake_case							) is not type(__snake_case							):
																							raise TypeError(
																							    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case							)} !='''
																							    f''' {type(__snake_case							)}.'''							)
																	elif isinstance(__snake_case							,  __snake_case							):
																							_snake_case: str	       =		[negative_prompt]
																	elif batch_size != len(__snake_case							):
																							raise ValueError(
																							    f'''`negative_prompt`: {negative_prompt} has batch size {len(__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:
																							_snake_case: Tuple	       =		negative_prompt

																	_snake_case: Tuple	       =		text_input_ids.shape[-1]
																	_snake_case: Dict	       =		self.tokenizer(
																	    __snake_case							,  padding='max_length'							,  max_length=__snake_case							,  truncation=__snake_case							,  return_tensors='pt'							,  )

																	# if hasattr(uncond_input, "attention_mask"):
																	#     attention_mask = uncond_input.attention_mask.to(device)
																	# else:
																	#     attention_mask = None

																	_snake_case: int	       =		self.text_encoder(
																	    input_ids=uncond_input.input_ids.int().to(__snake_case							)							,  )
																	_snake_case: List[str]	       =		uncond_embeddings[0]

											if do_classifier_free_guidance:
																	_snake_case: Dict	       =		uncond_embeddings.shape[1]
																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	_snake_case: Optional[int]	       =		uncond_embeddings.repeat(1							,  __snake_case							)
																	_snake_case: Dict	       =		uncond_embeddings.reshape(batch_size * num_images_per_prompt							,  __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
																	_snake_case: int	       =		np.concatenate([uncond_embeddings, prompt_embeds]							)

											return prompt_embeds












'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		query_dim

											return {
											    "query_dim": query_dim,
											    "cross_attention_dim": cross_attention_dim,
											    "heads": 2,
											    "dim_head": 4,
											    "added_kv_proj_dim": 6,
											    "norm_num_groups": 1,
											    "only_cross_attention": only_cross_attention,
											    "processor": AttnAddedKVProcessor(),
											}


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

											return {
											    "hidden_states": hidden_states,
											    "encoder_hidden_states": encoder_hidden_states,
											    "attention_mask": attention_mask,
											}




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is not None							)
											self.assertTrue(attn.to_v is not None							)

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is None							)
											self.assertTrue(attn.to_v is None							)

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

											_snake_case: int	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											# the initial conversion succeeds
											_snake_case: Dict	       =		DiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Any	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)
											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)





