

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			=       getLogger(__name__)


_a			=       4
_a			=       7

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







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]		)   ->   Dict:

	"""simple docstring"""






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

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

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

	return image






class 							A_			(		A__ ):


					def __init__(							self      :		List[str]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Tuple      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any] = 3_5_0      ,  )							->     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      :		Dict      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		int = 7_5      ,  UpperCAmelCase      :		Tuple = 9.0      ,  UpperCAmelCase      :		Tuple = 2_0      ,  UpperCAmelCase      :		Union[str, Any] = None      ,  UpperCAmelCase      :		Optional[int] = 1      ,  UpperCAmelCase      :		Optional[Any] = 0.0      ,  UpperCAmelCase      :		List[str] = None      ,  UpperCAmelCase      :		int = None      ,  UpperCAmelCase      :		int = None      ,  UpperCAmelCase      :		Tuple = None      ,  UpperCAmelCase      :		List[Any] = "pil"      ,  UpperCAmelCase      :		str = True      ,  UpperCAmelCase      :		Union[str, Any] = None      ,  UpperCAmelCase      :		List[str] = 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:       Union[str, Any]            =				1
						elif prompt is not None and isinstance(snake_case_      ,  snake_case_ ):
							__lowerCAmelCase:       Any            =				len(snake_case_ )
						else:
							__lowerCAmelCase:       int            =				prompt_embeds.shape[0]

						__lowerCAmelCase:       Tuple            =				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:       List[Any]            =				guidance_scale > 1.0

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

						__lowerCAmelCase:       Tuple            =				ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

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

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

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

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

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

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

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

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

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

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

								# compute the previous noisy sample x_t -> x_t-1
								__lowerCAmelCase:       Union[str, Any]            =				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:       str            =				self.decode_latents(latents.float() )

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

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=snake_case_ )


					def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Optional[int] )							->     Dict:
						__lowerCAmelCase:       Dict            =				1 / 0.08333 * latents
						__lowerCAmelCase:       Union[str, Any]            =				self.vae(latent_sample=snake_case_ )[0]
						__lowerCAmelCase:       Any            =				np.clip(image / 2 + 0.5      ,  0      ,  1 )
						__lowerCAmelCase:       str            =				image.transpose((0, 2, 3, 1) )
						return image


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

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

						__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Dict            =				prompt_embeds.shape
						# duplicate text embeddings for each generation per prompt, using mps friendly method
						__lowerCAmelCase:       Optional[int]            =				prompt_embeds.repeat(1      ,  snake_case_ )
						__lowerCAmelCase:       Dict            =				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:       Tuple            =				4_2
							if negative_prompt is None:
								__lowerCAmelCase:       Optional[Any]            =				[''] * 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:       List[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:
								__lowerCAmelCase:       List[str]            =				negative_prompt

							__lowerCAmelCase:       Any            =				text_input_ids.shape[-1]
							__lowerCAmelCase:       Any            =				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:       int            =				self.text_encoder(
							    input_ids=uncond_input.input_ids.int().to(snake_case_ )      ,  )
							__lowerCAmelCase:       Dict            =				uncond_embeddings[0]

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

						return prompt_embeds








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
_a			=       False
_a			=       Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a			=       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_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		str=sys.argv[1:] )							->     Optional[int]:
      __lowerCAmelCase:       List[Any]            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       Any            =				1
      __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				getopt.getopt(UpperCAmelCase      ,  'i:o:b:s'      ,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        __lowerCAmelCase:       Any            =				arg
       elif opt in ("-o", "--outfile"):
        __lowerCAmelCase:       List[Any]            =				arg
       elif opt in ("-b", "--batch_size"):
        __lowerCAmelCase:       List[Any]            =				int(UpperCAmelCase )
       elif opt in ("-s", "--subset_list"):
        __lowerCAmelCase:       Union[str, Any]            =				arg

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

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


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Dict )							->     Dict:
      __lowerCAmelCase:       str            =				int(file.split('.' )[0].split('_' )[-1] )
      __lowerCAmelCase:       Union[str, Any]            =				os.path.join(self.inputdir      ,  UpperCAmelCase )
      return (img_id, filepath)


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __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(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

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


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      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:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				stream.read().split('\n'		)
 return data


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





