
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


SCREAMING_SNAKE_CASE_			      =					getLogger(__name__)


SCREAMING_SNAKE_CASE_			      =					4
SCREAMING_SNAKE_CASE_			      =					7

SCREAMING_SNAKE_CASE_			      =					{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}

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

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

												__lowerCAmelCase        =       [np.array(i.resize((w, h)		)		)[None, :] for i in image]
												__lowerCAmelCase        =       np.concatenate(_lowerCAmelCase				,  axis=0		)
												__lowerCAmelCase        =       np.array(_lowerCAmelCase		).astype(np.floataa		) / 255.0
												__lowerCAmelCase        =       image.transpose(0				,  3				,  1				,  2		)
												__lowerCAmelCase        =       2.0 * image - 1.0
												__lowerCAmelCase        =       torch.from_numpy(_lowerCAmelCase		)
						elif isinstance(image[0]				,  torch.Tensor		):
												__lowerCAmelCase        =       torch.cat(_lowerCAmelCase				,  dim=0		)

						return image



class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = 350  ,							)   ->       List[str]:
													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  ,							snake_case_  ,							snake_case_  ,							snake_case_ = 75  ,							snake_case_ = 9.0  ,							snake_case_ = 20  ,							snake_case_ = None  ,							snake_case_ = 1  ,							snake_case_ = 0.0  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = "pil"  ,							snake_case_ = True  ,							snake_case_ = None  ,							snake_case_ = 1  ,							)   ->       Any:
													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_							):
																			__lowerCAmelCase        =       1
													elif prompt is not None and isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       len(snake_case_							)
													else:
																			__lowerCAmelCase        =       prompt_embeds.shape[0]

													__lowerCAmelCase        =       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.
													__lowerCAmelCase        =       guidance_scale > 1.0

													# 3. Encode input prompt
													__lowerCAmelCase        =       self._encode_prompt(
													    snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							prompt_embeds=snake_case_  ,							negative_prompt_embeds=snake_case_  ,							)

													__lowerCAmelCase        =       ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

													# 4. Preprocess image
													__lowerCAmelCase        =       preprocess(snake_case_							)
													__lowerCAmelCase        =       image.cpu()

													# 5. set timesteps
													self.scheduler.set_timesteps(snake_case_  ,							device=snake_case_							)
													__lowerCAmelCase        =       self.scheduler.timesteps

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

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

													# 6. Prepare latent variables
													__lowerCAmelCase ,      __lowerCAmelCase        =       image.shape[2:]
													__lowerCAmelCase        =       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
													__lowerCAmelCase        =       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
													__lowerCAmelCase        =       self.prepare_extra_step_kwargs(snake_case_  ,							snake_case_							)

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

													# 9. Denoising loop
													__lowerCAmelCase        =       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
																									__lowerCAmelCase        =       np.concatenate([latents] * 2							) if do_classifier_free_guidance else latents

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

																									# timestep to tensor
																									__lowerCAmelCase        =       np.array([t]  ,							dtype=snake_case_							)

																									# predict the noise residual
																									__lowerCAmelCase        =       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:
																															__lowerCAmelCase ,      __lowerCAmelCase        =       np.split(snake_case_  ,							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(
																									    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
													__lowerCAmelCase        =       self.decode_latents(latents.float()							)

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       Dict:
													__lowerCAmelCase        =       1 / 0.08_333 * latents
													__lowerCAmelCase        =       self.vae(latent_sample=snake_case_							)[0]
													__lowerCAmelCase        =       np.clip(image / 2 + 0.5  ,							0  ,							1							)
													__lowerCAmelCase        =       image.transpose((0, 2, 3, 1)							)
													return image




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       List[str]:
													if prompt is not None and isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       1
													elif prompt is not None and isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       len(snake_case_							)
													else:
																			__lowerCAmelCase        =       prompt_embeds.shape[0]

													if prompt_embeds is None:
																			__lowerCAmelCase        =       self.tokenizer(
																			    snake_case_  ,							padding="""max_length"""  ,							max_length=self.tokenizer.model_max_length  ,							truncation=snake_case_  ,							return_tensors="""pt"""  ,							)
																			__lowerCAmelCase        =       text_inputs.input_ids
																			__lowerCAmelCase        =       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_							):
																									__lowerCAmelCase        =       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
																			__lowerCAmelCase        =       self.text_encoder(
																			    input_ids=text_input_ids.int().to(snake_case_							)  ,							)
																			__lowerCAmelCase        =       prompt_embeds[0]

													__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       prompt_embeds.shape
													# duplicate text embeddings for each generation per prompt, using mps friendly method
													__lowerCAmelCase        =       prompt_embeds.repeat(1  ,							snake_case_							)
													__lowerCAmelCase        =       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:
																			__lowerCAmelCase        =       42
																			if negative_prompt is None:
																									__lowerCAmelCase        =       [""""""] * 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_							):
																									__lowerCAmelCase        =       [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:
																									__lowerCAmelCase        =       negative_prompt

																			__lowerCAmelCase        =       text_input_ids.shape[-1]
																			__lowerCAmelCase        =       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

																			__lowerCAmelCase        =       self.text_encoder(
																			    input_ids=uncond_input.input_ids.int().to(snake_case_							)  ,							)
																			__lowerCAmelCase        =       uncond_embeddings[0]

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

													return prompt_embeds


"""simple docstring"""
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
			import torch
class      lowerCAmelCase_						(							unittest.TestCase							,							A__				):



							'''simple docstring'''





							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       load_tool("""speech-to-text"""							)
													self.tool.setup()




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       self.tool(torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.tool(audio=torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)
