
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


lowercase__						:List[str]					    =       getLogger(__name__)


lowercase__						:List[str]					    =       4
lowercase__						:Any					    =       7

lowercase__						:Tuple					    =       {
    "float16": torch.floataa,
    "float32": torch.floataa,
}



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''







					if isinstance(__snake_case					,     torch.Tensor						):
										return image
					elif isinstance(__snake_case					,     PIL.Image.Image						):
										lowercase		      =		[image]

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

										lowercase		      =		[np.array(i.resize((w, h)						)						)[None, :] for i in image]
										lowercase		      =		np.concatenate(__snake_case					,     axis=0						)
										lowercase		      =		np.array(__snake_case						).astype(np.floataa						) / 2_55.0
										lowercase		      =		image.transpose(0					,     3					,     1					,     2						)
										lowercase		      =		2.0 * image - 1.0
										lowercase		      =		torch.from_numpy(__snake_case						)
					elif isinstance(image[0]					,     torch.Tensor						):
										lowercase		      =		torch.cat(__snake_case					,     dim=0						)

					return image







class 							lowercase						(   lowercase__						):




							def __init__(							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__ = 3_5_0  ,):
												super().__init__(
												    vae=A__  ,text_encoder=A__  ,tokenizer=A__  ,unet=A__  ,low_res_scheduler=A__  ,scheduler=A__  ,safety_checker=A__  ,feature_extractor=A__  ,watermarker=A__  ,max_noise_level=A__  ,)




							def __call__(							self  ,A__  ,A__  ,A__ = 7_5  ,A__ = 9.0  ,A__ = 2_0  ,A__ = None  ,A__ = 1  ,A__ = 0.0  ,A__ = None  ,A__ = None  ,A__ = None  ,A__ = None  ,A__ = "pil"  ,A__ = True  ,A__ = None  ,A__ = 1  ,):
												self.check_inputs(A__  ,A__  ,A__  ,A__)

												# 2. Define call parameters
												if prompt is not None and isinstance(A__  ,A__):
																	lowercase		      =		1
												elif prompt is not None and isinstance(A__  ,A__):
																	lowercase		      =		len(A__)
												else:
																	lowercase		      =		prompt_embeds.shape[0]

												lowercase		      =		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.
												lowercase		      =		guidance_scale > 1.0

												# 3. Encode input prompt
												lowercase		      =		self._encode_prompt(
												    A__  ,A__  ,A__  ,A__  ,A__  ,prompt_embeds=A__  ,negative_prompt_embeds=A__  ,)

												lowercase		      =		ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

												# 4. Preprocess image
												lowercase		      =		preprocess(A__)
												lowercase		      =		image.cpu()

												# 5. set timesteps
												self.scheduler.set_timesteps(A__  ,device=A__)
												lowercase		      =		self.scheduler.timesteps

												# 5. Add noise to image
												lowercase		      =		torch.tensor([noise_level]  ,dtype=torch.long  ,device=A__)
												lowercase		      =		torch.randn(image.shape  ,generator=A__  ,device=A__  ,dtype=A__)
												lowercase		      =		self.low_res_scheduler.add_noise(A__  ,A__  ,A__)

												lowercase		      =		2 if do_classifier_free_guidance else 1
												lowercase		      =		np.concatenate([image] * batch_multiplier * num_images_per_prompt)
												lowercase		      =		np.concatenate([noise_level] * image.shape[0])

												# 6. Prepare latent variables
												lowercase		      =		image.shape[2:]
												lowercase		      =		self.prepare_latents(
												    batch_size * num_images_per_prompt  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__  ,)

												# 7. Check that sizes of image and latents match
												lowercase		      =		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
												lowercase		      =		self.prepare_extra_step_kwargs(A__  ,A__)

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

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

																						# concat latents, mask, masked_image_latents in the channel dimension
																						lowercase		      =		self.scheduler.scale_model_input(A__  ,A__)
																						lowercase		      =		np.concatenate([latent_model_input, image]  ,axis=1)

																						# timestep to tensor
																						lowercase		      =		np.array([t]  ,dtype=A__)

																						# predict the noise residual
																						lowercase		      =		self.unet(
																						    sample=A__  ,timestep=A__  ,encoder_hidden_states=A__  ,class_labels=noise_level.astype(np.intaa)  ,)[0]

																						# perform guidance
																						if do_classifier_free_guidance:
																											lowercase		      =		np.split(A__  ,2)
																						lowercase		      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						# compute the previous noisy sample x_t -> x_t-1
																						lowercase		      =		self.scheduler.step(
																						    torch.from_numpy(A__)  ,A__  ,A__  ,**A__).prev_sample

																						# call the callback, if provided
																						if i == len(A__) - 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(A__  ,A__  ,A__)

        # 10. Post-processing
												lowercase		      =		self.decode_latents(latents.float())

												# 11. Convert to PIL
												if output_type == "pil":
																	lowercase		      =		self.numpy_to_pil(A__)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__)




							def 					A__ (							self  ,A__):
												lowercase		      =		1 / 0.08333 * latents
												lowercase		      =		self.vae(latent_sample=A__)[0]
												lowercase		      =		np.clip(image / 2 + 0.5  ,0  ,1)
												lowercase		      =		image.transpose((0, 2, 3, 1))
												return image





							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__ = None  ,A__ = None  ,):





												if prompt is not None and isinstance(A__  ,A__):
																	lowercase		      =		1
												elif prompt is not None and isinstance(A__  ,A__):
																	lowercase		      =		len(A__)
												else:
																	lowercase		      =		prompt_embeds.shape[0]

												if prompt_embeds is None:
																	lowercase		      =		self.tokenizer(
																	    A__  ,padding='''max_length'''  ,max_length=self.tokenizer.model_max_length  ,truncation=A__  ,return_tensors='''pt'''  ,)
																	lowercase		      =		text_inputs.input_ids
																	lowercase		      =		self.tokenizer(A__  ,padding='''longest'''  ,return_tensors='''pt''').input_ids

																	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																	    A__  ,A__):
																						lowercase		      =		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
																	lowercase		      =		self.text_encoder(
																	    input_ids=text_input_ids.int().to(A__)  ,)
																	lowercase		      =		prompt_embeds[0]

												lowercase		      =		prompt_embeds.shape
												# duplicate text embeddings for each generation per prompt, using mps friendly method
												lowercase		      =		prompt_embeds.repeat(1  ,A__)
												lowercase		      =		prompt_embeds.reshape(bs_embed * num_images_per_prompt  ,A__  ,-1)

												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance and negative_prompt_embeds is None:
																	lowercase		      =		42
																	if negative_prompt is None:
																						lowercase		      =		[""] * 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__):
																						lowercase		      =		[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:
																						lowercase		      =		negative_prompt

																	lowercase		      =		text_input_ids.shape[-1]
																	lowercase		      =		self.tokenizer(
																	    A__  ,padding='''max_length'''  ,max_length=A__  ,truncation=A__  ,return_tensors='''pt'''  ,)

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

																	lowercase		      =		self.text_encoder(
																	    input_ids=uncond_input.input_ids.int().to(A__)  ,)
																	lowercase		      =		uncond_embeddings[0]

												if do_classifier_free_guidance:
																	lowercase		      =		uncond_embeddings.shape[1]
																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowercase		      =		uncond_embeddings.repeat(1  ,A__)
																	lowercase		      =		uncond_embeddings.reshape(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
																	lowercase		      =		np.concatenate([uncond_embeddings, prompt_embeds])

												return prompt_embeds









import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__lowerCamelCase								=      [(1,)]







def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       @slow
       @require_torch_gpu
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @require_torch_gpu
       @slow
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @parameterized.expand(lowercase)
       @slow
       def 			A_ (  self  ,lowercase=None):

             """simple docstring"""





             check_slow()







@slow
@require_torch_gpu
def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      check_slow_torch_cuda()







@require_torch_gpu
@slow
def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("param"       ,	[1]     )
def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      check_slow()







@pytest.mark.parametrize("param"       ,	[1]     )
@slow
def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      check_slow()
