

"""simple docstring"""





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      torch.FloatTensor
 __UpperCAmelCase			:      torch.FloatTensor







def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Tuple=0.999,    UpperCamelCase_  : Optional[Any]="cosine",    )			->		int:
   '''simple docstring'''


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCamelCase_  : Optional[Any]):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCamelCase_  : List[Any]):
         return math.exp(t * -12.0)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""")

   __lowercase			=		[]
   for i in range(UpperCamelCase_):
      __lowercase			=		i / num_diffusion_timesteps
      __lowercase			=		(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCamelCase_) / alpha_bar_fn(UpperCamelCase_),    UpperCamelCase_))
   return torch.tensor(UpperCamelCase_,    dtype=torch.floataa)




class       _lowerCAmelCase						(			lowercase  ,lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Union[str, Any]     	=			1


 @register_to_config
 def __init__(    self							:    Optional[Any],      UpperCAmelCase__							:    int = 1_0_0_0,      UpperCAmelCase__							:    float = 0.0_001,      UpperCAmelCase__							:    float = 0.02,      UpperCAmelCase__							:    str = "linear",      UpperCAmelCase__							:    float = 0.0,      UpperCAmelCase__							:    Optional[np.ndarray] = None,      UpperCAmelCase__							:    bool = True,      ):
    if trained_betas is not None:
       __lowercase			=		torch.from_numpy(UpperCAmelCase__			)
    elif beta_schedule == "linear":
       __lowercase			=		torch.linspace(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      dtype=torch.floataa			)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowercase			=		(
           torch.linspace(beta_start**0.5,      beta_end**0.5,      UpperCAmelCase__,      dtype=torch.floataa			) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowercase			=		betas_for_alpha_bar(UpperCAmelCase__			)
    elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       __lowercase			=		torch.linspace(-6,      6,      UpperCAmelCase__			)
       __lowercase			=		torch.sigmoid(UpperCAmelCase__			) * (beta_end - beta_start) + beta_start
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""			)

    __lowercase			=		1.0 - self.betas
    __lowercase			=		torch.cumprod(self.alphas,      dim=0			)
    __lowercase			=		torch.tensor(1.0			)

    __lowercase			=		torch.tensor(1.0			)

    # standard deviation of the initial noise distribution
    __lowercase			=		1.0

    # setable values
    __lowercase			=		None
    __lowercase			=		torch.from_numpy(np.arange(0,      UpperCAmelCase__			)[::-1].copy()			)

    __lowercase			=		eta


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    Optional[int] = None			):
    return sample


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int = 1_0,      UpperCAmelCase__							:    int = 1_0,      UpperCAmelCase__							:    Union[str, torch.device] = None,      ):
    __lowercase			=		min(self.config.num_train_timesteps,      UpperCAmelCase__			)
    __lowercase			=		num_inference_steps

    __lowercase			=		[]

    __lowercase			=		{}
    for j in range(0,      num_inference_steps - jump_length,      UpperCAmelCase__			):
       __lowercase			=		jump_n_sample - 1

    __lowercase			=		num_inference_steps
    while t >= 1:
       __lowercase			=		t - 1
       timesteps.append(UpperCAmelCase__			)

       if jumps.get(UpperCAmelCase__,      0			) > 0:
          __lowercase			=		jumps[t] - 1
          for _ in range(UpperCAmelCase__			):
             __lowercase			=		t + 1
             timesteps.append(UpperCAmelCase__			)

    __lowercase			=		np.array(UpperCAmelCase__			) * (self.config.num_train_timesteps // self.num_inference_steps)
    __lowercase			=		torch.from_numpy(UpperCAmelCase__			).to(UpperCAmelCase__			)


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    List[str]			):
    __lowercase			=		t - self.config.num_train_timesteps // self.num_inference_steps

    __lowercase			=		self.alphas_cumprod[t]
    __lowercase			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    __lowercase			=		1 - alpha_prod_t
    __lowercase			=		1 - alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from
    # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
    # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
    # variance to pred_sample
    # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
    # without eta.
    # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
    __lowercase			=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

    return variance


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    Optional[torch.Generator] = None,      UpperCAmelCase__							:    bool = True,      ):
    __lowercase			=		timestep
    __lowercase			=		timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 1. compute alphas, betas
    __lowercase			=		self.alphas_cumprod[t]
    __lowercase			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    __lowercase			=		1 - alpha_prod_t

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    __lowercase			=		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       __lowercase			=		torch.clamp(UpperCAmelCase__,      -1,      1			)

    # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
    # substitute formula (7) in the algorithm coming from DDPM paper
    # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
    # DDIM schedule gives the same results as DDPM with eta = 1.0
    # Noise is being reused in 7. and 8., but no impact on quality has
    # been observed.

    # 5. Add noise
    __lowercase			=		model_output.device
    __lowercase			=		randn_tensor(model_output.shape,      generator=UpperCAmelCase__,      device=UpperCAmelCase__,      dtype=model_output.dtype			)
    __lowercase			=		self.eta * self._get_variance(UpperCAmelCase__			) ** 0.5

    __lowercase			=		0
    if t > 0 and self.eta > 0:
       __lowercase			=		std_dev_t * noise

    # 6. compute "direction pointing to x_t" of formula (12)
    # from https://arxiv.org/pdf/2010.02502.pdf
    __lowercase			=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __lowercase			=		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

    # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
    __lowercase			=		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

    # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
    __lowercase			=		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

    if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

    return RePaintSchedulerOutput(prev_sample=UpperCAmelCase__,      pred_original_sample=UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Tuple=None			):
    __lowercase			=		self.config.num_train_timesteps // self.num_inference_steps

    for i in range(UpperCAmelCase__			):
       __lowercase			=		self.betas[timestep + i]
       if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          __lowercase			=		randn_tensor(sample.shape,      dtype=sample.dtype,      generator=UpperCAmelCase__			)
          __lowercase			=		noise.to(sample.device			)
       else:
          __lowercase			=		randn_tensor(sample.shape,      generator=UpperCAmelCase__,      device=sample.device,      dtype=sample.dtype			)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       __lowercase			=		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

    return sample


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.IntTensor,      ):
    raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)



 def __len__(    self							:    List[str]			):
    return self.config.num_train_timesteps







"""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
_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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)




class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]=sys.argv[1:]			):
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		1
    __lowercase	,__lowercase			=		getopt.getopt(UpperCAmelCase__,      "i:o:b:s",      ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
    for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
          __lowercase			=		arg
       elif opt in ("-o", "--outfile"):
          __lowercase			=		arg
       elif opt in ("-b", "--batch_size"):
          __lowercase			=		int(UpperCAmelCase__			)
       elif opt in ("-s", "--subset_list"):
          __lowercase			=		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:
          __lowercase			=		{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase__			)}
    else:
       __lowercase			=		None

    __lowercase			=		CONFIG
    if torch.cuda.is_available():
       __lowercase			=		"cuda"
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __lowercase			=		Preprocess(self.config			)
    __lowercase			=		GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned",      config=self.config			)
    __lowercase			=		batch_size if batch_size != 0 else 1
    __lowercase			=		DEFAULT_SCHEMA


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any]			):
    __lowercase			=		int(file.split("."			)[0].split("_"			)[-1]			)
    __lowercase			=		os.path.join(self.inputdir,      UpperCAmelCase__			)
    return (img_id, filepath)


 @property
 def       _lowercase				(    self							:    int			):
    __lowercase			=		[]
    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:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



 def __call__(    self							:    Optional[Any]			):
    # make writer
    if not TEST:
       __lowercase			=		datasets.ArrowWriter(features=self.schema,      path=self.outputfile			)
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator			):
       __lowercase	,__lowercase	,__lowercase			=		self.preprocess(UpperCAmelCase__			)
       __lowercase			=		self.model(
           UpperCAmelCase__,      UpperCAmelCase__,      scales_yx=UpperCAmelCase__,      padding="max_detections",      max_detections=self.config.MAX_DETECTIONS,      pad_value=0,      return_tensors="np",      location="cpu",      )
       __lowercase			=		output_dict.pop("normalized_boxes"			)
       if not TEST:
          __lowercase			=		np.array(UpperCAmelCase__			)
          __lowercase			=		self.schema.encode_batch(UpperCAmelCase__			)
          writer.write_batch(UpperCAmelCase__			)
       if TEST:
          break
       # finalizer the writer
    if not TEST:
       __lowercase	,__lowercase			=		writer.finalize()
       print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      try:
         __lowercase			=		list(data.keys())
      except Exception:
         __lowercase			=		[d["img_id"] for d in data]
   except Exception:
      try:
         __lowercase			=		eval(stream.read())
      except Exception:
         __lowercase			=		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)




