

"""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


_UpperCamelCase		    =						getLogger(__name__)


_UpperCamelCase		    =						4
_UpperCamelCase		    =						7

_UpperCamelCase		    =						{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Union[str, Any]:
 if isinstance(UpperCamelCase__				,				torch.Tensor      ):
  return image
 elif isinstance(UpperCamelCase__				,				PIL.Image.Image      ):
  lowerCAmelCase__      :							Dict    =							[image]

 if isinstance(image[0]				,				PIL.Image.Image      ):
  lowerCAmelCase__      :							List[Any]    =							image[0].size
  lowerCAmelCase__      :							Optional[Any]    =							(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

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

 return image







class  __a				(	__magic_name__					):





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case = 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 ,				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 ,				):





        """simple docstring"""

        self.check_inputs(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase      )

        # 2. Define call parameters
        if prompt is not None and isinstance(__lowerCamelCase ,				__lowerCamelCase      ):
         lowerCAmelCase__      :							Optional[Any]    =							1
        elif prompt is not None and isinstance(__lowerCamelCase ,				__lowerCamelCase      ):
         lowerCAmelCase__      :							Tuple    =							len(__lowerCamelCase      )
        else:
         lowerCAmelCase__      :							Optional[Any]    =							prompt_embeds.shape[0]

        lowerCAmelCase__      :							Optional[int]    =							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__      :							Optional[Any]    =							guidance_scale > 1.0

        # 3. Encode input prompt
        lowerCAmelCase__      :							Tuple    =							self._encode_prompt(
            __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				prompt_embeds=__lowerCamelCase ,				negative_prompt_embeds=__lowerCamelCase ,				)

        lowerCAmelCase__      :							List[str]    =							ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

        # 4. Preprocess image
        lowerCAmelCase__      :							int    =							preprocess(__lowerCamelCase      )
        lowerCAmelCase__      :							Tuple    =							image.cpu()

        # 5. set timesteps
        self.scheduler.set_timesteps(__lowerCamelCase ,				device=__lowerCamelCase      )
        lowerCAmelCase__      :							str    =							self.scheduler.timesteps

        # 5. Add noise to image
        lowerCAmelCase__      :							int    =							torch.tensor([noise_level] ,				dtype=torch.long ,				device=__lowerCamelCase      )
        lowerCAmelCase__      :							List[str]    =							torch.randn(image.shape ,				generator=__lowerCamelCase ,				device=__lowerCamelCase ,				dtype=__lowerCamelCase      )
        lowerCAmelCase__      :							List[str]    =							self.low_res_scheduler.add_noise(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase      )

        lowerCAmelCase__      :							Union[str, Any]    =							2 if do_classifier_free_guidance else 1
        lowerCAmelCase__      :							Optional[Any]    =							np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
        lowerCAmelCase__      :							Tuple    =							np.concatenate([noise_level] * image.shape[0]      )

        # 6. Prepare latent variables
        lowerCAmelCase__      :							Any    =							image.shape[2:]
        lowerCAmelCase__      :							Dict    =							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
        lowerCAmelCase__      :							List[str]    =							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__      :							Any    =							self.prepare_extra_step_kwargs(__lowerCamelCase ,				__lowerCamelCase      )

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

        # 9. Denoising loop
        lowerCAmelCase__      :							str    =							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
          lowerCAmelCase__      :							Dict    =							np.concatenate([latents] * 2      ) if do_classifier_free_guidance else latents

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

          # timestep to tensor
          lowerCAmelCase__      :							Optional[int]    =							np.array([t] ,				dtype=__lowerCamelCase      )

          # predict the noise residual
          lowerCAmelCase__      :							str    =							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:
           lowerCAmelCase__      :							Optional[int]    =							np.split(__lowerCamelCase ,				2      )
          lowerCAmelCase__      :							List[Any]    =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase__      :							Dict    =							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
        lowerCAmelCase__      :							List[Any]    =							self.decode_latents(latents.float()      )

        # 11. Convert to PIL
        if output_type == "pil":
         lowerCAmelCase__      :							List[str]    =							self.numpy_to_pil(__lowerCamelCase      )

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__lowerCamelCase      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							1 / 0.08_333 * latents
        lowerCAmelCase__      :							List[Any]    =							self.vae(latent_sample=__lowerCamelCase      )[0]
        lowerCAmelCase__      :							Any    =							np.clip(image / 2 + 0.5 ,				0 ,				1      )
        lowerCAmelCase__      :							Any    =							image.transpose((0, 2, 3, 1)      )
        return image






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case = None ,				snake_case = None ,				):





        """simple docstring"""

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

        if prompt_embeds is None:
         lowerCAmelCase__      :							List[str]    =							self.tokenizer(
             __lowerCamelCase ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				truncation=__lowerCamelCase ,				return_tensors="pt" ,				)
         lowerCAmelCase__      :							Dict    =							text_inputs.input_ids
         lowerCAmelCase__      :							Any    =							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      ):
          lowerCAmelCase__      :							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
         lowerCAmelCase__      :							int    =							self.text_encoder(
             input_ids=text_input_ids.int().to(__lowerCamelCase      ) ,				)
         lowerCAmelCase__      :							List[Any]    =							prompt_embeds[0]

        lowerCAmelCase__      :							Dict    =							prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        lowerCAmelCase__      :							Optional[Any]    =							prompt_embeds.repeat(1 ,				__lowerCamelCase      )
        lowerCAmelCase__      :							Optional[Any]    =							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:
         lowerCAmelCase__      :							List[str]
         if negative_prompt is None:
          lowerCAmelCase__      :							int    =							[""] * 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      ):
          lowerCAmelCase__      :							Union[str, Any]    =							[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:
          lowerCAmelCase__      :							List[str]    =							negative_prompt

         lowerCAmelCase__      :							str    =							text_input_ids.shape[-1]
         lowerCAmelCase__      :							List[Any]    =							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

         lowerCAmelCase__      :							int    =							self.text_encoder(
             input_ids=uncond_input.input_ids.int().to(__lowerCamelCase      ) ,				)
         lowerCAmelCase__      :							Any    =							uncond_embeddings[0]

        if do_classifier_free_guidance:
         lowerCAmelCase__      :							List[Any]    =							uncond_embeddings.shape[1]
         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         lowerCAmelCase__      :							Tuple    =							uncond_embeddings.repeat(1 ,				__lowerCamelCase      )
         lowerCAmelCase__      :							List[Any]    =							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
         lowerCAmelCase__      :							Union[str, Any]    =							np.concatenate([uncond_embeddings, prompt_embeds]      )

        return prompt_embeds









"""simple docstring"""

import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_UpperCamelCase		    =						False
_UpperCamelCase		    =						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase		    =						datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(snake_case      ), F"""{outputfile}"""
        if subset_list is not None:
         with open(os.path.realpath(snake_case      )      ) as f:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

        lowerCAmelCase__      :							Optional[int]    =							CONFIG
        if torch.cuda.is_available():
         lowerCAmelCase__      :							Union[str, Any]    =							"cuda"
        lowerCAmelCase__      :							Dict    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Tuple    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Any    =							Preprocess(self.config      )
        lowerCAmelCase__      :							str    =							GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned" ,				config=self.config      )
        lowerCAmelCase__      :							int    =							batch_size if batch_size != 0 else 1
        lowerCAmelCase__      :							Optional[int]    =							DEFAULT_SCHEMA






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							[]
        for i, file in enumerate(os.listdir(self.inputdir      )      ):
         if self.subset_list is not None and i not in self.subset_list:
          continue
         batch.append(self._vqa_file_split(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

        for i in range(1      ):
         yield list(map(snake_case ,				zip(*snake_case      )      )      )






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=snake_case ,				padding="max_detections" ,				max_detections=self.config.MAX_DETECTIONS ,				pad_value=0 ,				return_tensors="np" ,				location="cpu" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


if __name__ == "__main__":
    _UpperCamelCase		    =						Extract(sys.argv[1:])
    extract()
    if not TEST:
        _UpperCamelCase		    =						datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)






