



'''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


_lowercase:				str       =	getLogger(__name__)


_lowercase:				Tuple       =	4
_lowercase:				str       =	7

_lowercase:				int       =	{
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def 					a(   A :					Any )	-> Dict:
					"""simple docstring"""





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

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

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

					return image







class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 350	, ):
						"""simple docstring"""

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




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 75	, lowerCamelCase_ = 9.0	, lowerCamelCase_ = 20	, lowerCamelCase_ = None	, lowerCamelCase_ = 1	, lowerCamelCase_ = 0.0	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = "pil"	, lowerCamelCase_ = True	, lowerCamelCase_ = None	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						self.check_inputs(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

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

						a        =     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.
						a        =     guidance_scale > 1.0

						# 3. Encode input prompt
						a        =     self._encode_prompt(
						    lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, prompt_embeds=lowerCamelCase_	, negative_prompt_embeds=lowerCamelCase_	, )

						a        =     ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]

						# 4. Preprocess image
						a        =     preprocess(lowerCamelCase_	)
						a        =     image.cpu()

						# 5. set timesteps
						self.scheduler.set_timesteps(lowerCamelCase_	, device=lowerCamelCase_	)
						a        =     self.scheduler.timesteps

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

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

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

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

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

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

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

																# timestep to tensor
																a        =     np.array([t]	, dtype=lowerCamelCase_	)

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

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

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

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

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

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

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     1 / 0.0_8333 * latents
						a        =     self.vae(latent_sample=lowerCamelCase_	)[0]
						a        =     np.clip(image / 2 + 0.5	, 0	, 1	)
						a        =     image.transpose((0, 2, 3, 1)	)
						return image




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

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

						if prompt_embeds is None:
											a        =     self.tokenizer(
											    lowerCamelCase_	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=lowerCamelCase_	, return_tensors="pt"	, )
											a        =     text_inputs.input_ids
											a        =     self.tokenizer(lowerCamelCase_	, padding="longest"	, return_tensors="pt"	).input_ids

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

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

						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance and negative_prompt_embeds is None:
											a        =     42
											if negative_prompt is None:
																a        =     [""] * batch_size
											elif type(lowerCamelCase_	) is not type(lowerCamelCase_	):
																raise TypeError(
																    F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_	)} !='''
																    F''' {type(lowerCamelCase_	)}.'''	)
											elif isinstance(lowerCamelCase_	, lowerCamelCase_	):
																a        =     [negative_prompt]
											elif batch_size != len(lowerCamelCase_	):
																raise ValueError(
																    F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_	)}, but `prompt`:'''
																    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																    " the batch size of `prompt`."	)
											else:
																a        =     negative_prompt

											a        =     text_input_ids.shape[-1]
											a        =     self.tokenizer(
											    lowerCamelCase_	, padding="max_length"	, max_length=lowerCamelCase_	, truncation=lowerCamelCase_	, return_tensors="pt"	, )

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

											a        =     self.text_encoder(
											    input_ids=uncond_input.input_ids.int().to(lowerCamelCase_	)	, )
											a        =     uncond_embeddings[0]

						if do_classifier_free_guidance:
											a        =     uncond_embeddings.shape[1]
											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											a        =     uncond_embeddings.repeat(1	, lowerCamelCase_	)
											a        =     uncond_embeddings.reshape(batch_size * num_images_per_prompt	, lowerCamelCase_	, -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
											a        =     np.concatenate([uncond_embeddings, prompt_embeds]	)

						return prompt_embeds





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
