

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
_lowerCamelCase							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, Any]     =				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    __UpperCAmelCase	:



   def __init__(				self				:	List[str],						__A				:	int=sys.argv[1:]		):
     UpperCAmelCase			:				List[str]										=					None
     UpperCAmelCase			:				Optional[Any]										=					None
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Any										=					1
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					getopt.getopt(__A,						'''i:o:b:s''',						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         UpperCAmelCase			:				Dict										=					arg
       elif opt in ("-o", "--outfile"):
         UpperCAmelCase			:				Optional[int]										=					arg
       elif opt in ("-b", "--batch_size"):
         UpperCAmelCase			:				Tuple										=					int(__A		)
       elif opt in ("-s", "--subset_list"):
         UpperCAmelCase			:				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(__A		), F'''{outputfile}'''
     if subset_list is not None:
       with open(os.path.realpath(__A		)		) as f:
         UpperCAmelCase			:				Optional[Any]										=					{self._vqa_file_split()[0] for x in tryload(__A		)}
     else:
       UpperCAmelCase			:				Tuple										=					None

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



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



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     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(__A		)		)
       if len(__A		) == self.batch:
         UpperCAmelCase			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

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





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






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


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








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    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	42
   UpperCamelCase  	=	42






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]=0.999							,					UpperCAmelCase				:     Tuple="cosine"							,					)    ->		List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase				:     List[str]     ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCAmelCase				:     int     ):
      return math.exp(t * -12.0     )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''     )

  UpperCAmelCase			:				Dict										=					[]
  for i in range(UpperCAmelCase     ):
    UpperCAmelCase			:				Any										=					i / num_diffusion_timesteps
    UpperCAmelCase			:				List[Any]										=					(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    __UpperCAmelCase	(			lowerCamelCase__     ,    lowerCamelCase__		):
   UpperCamelCase  	=	1



   @register_to_config
   def __init__(				self				:	str,						__A				:	int = 1_0_0_0,						__A				:	float = 0.0_0_0_1,						__A				:	float = 0.0_2,						__A				:	str = "linear",						__A				:	float = 0.0,						__A				:	Optional[np.ndarray] = None,						__A				:	bool = True,						):
     if trained_betas is not None:
       UpperCAmelCase			:				int										=					torch.from_numpy(__A		)
     elif beta_schedule == "linear":
       UpperCAmelCase			:				str										=					torch.linspace(__A,						__A,						__A,						dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:				List[str]										=					(
           torch.linspace(beta_start**0.5,						beta_end**0.5,						__A,						dtype=torch.floataa		) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase			:				Tuple										=					betas_for_alpha_bar(__A		)
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       UpperCAmelCase			:				Any										=					torch.linspace(-6,						6,						__A		)
       UpperCAmelCase			:				Tuple										=					torch.sigmoid(__A		) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

     UpperCAmelCase			:				Optional[int]										=					1.0 - self.betas
     UpperCAmelCase			:				int										=					torch.cumprod(self.alphas,						dim=0		)
     UpperCAmelCase			:				int										=					torch.tensor(1.0		)

     UpperCAmelCase			:				Union[str, Any]										=					torch.tensor(1.0		)

     # standard deviation of the initial noise distribution
     UpperCAmelCase			:				str										=					1.0

     # setable values
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Dict										=					torch.from_numpy(np.arange(0,						__A		)[::-1].copy()		)

     UpperCAmelCase			:				List[str]										=					eta



   def 			__magic_name__       (				self				:	List[str],						__A				:	torch.FloatTensor,						__A				:	Optional[int] = None		):
     return sample



   def 			__magic_name__       (				self				:	List[str],						__A				:	int,						__A				:	int = 1_0,						__A				:	int = 1_0,						__A				:	Union[str, torch.device] = None,						):
     UpperCAmelCase			:				Union[str, Any]										=					min(self.config.num_train_timesteps,						__A		)
     UpperCAmelCase			:				Optional[int]										=					num_inference_steps

     UpperCAmelCase			:				Union[str, Any]										=					[]

     UpperCAmelCase			:				Any										=					{}
     for j in range(0,						num_inference_steps - jump_length,						__A		):
       UpperCAmelCase			:				Tuple										=					jump_n_sample - 1

     UpperCAmelCase			:				str										=					num_inference_steps
     while t >= 1:
       UpperCAmelCase			:				Dict										=					t - 1
       timesteps.append(__A		)

       if jumps.get(__A,						0		) > 0:
         UpperCAmelCase			:				Dict										=					jumps[t] - 1
         for _ in range(__A		):
           UpperCAmelCase			:				Any										=					t + 1
           timesteps.append(__A		)

     UpperCAmelCase			:				Optional[Any]										=					np.array(__A		) * (self.config.num_train_timesteps // self.num_inference_steps)
     UpperCAmelCase			:				Dict										=					torch.from_numpy(__A		).to(__A		)



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Any		):
     UpperCAmelCase			:				int										=					t - self.config.num_train_timesteps // self.num_inference_steps

     UpperCAmelCase			:				Optional[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				Union[str, Any]										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				List[Any]										=					1 - alpha_prod_t
     UpperCAmelCase			:				str										=					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]
     UpperCAmelCase			:				Union[str, Any]										=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance



   def 			__magic_name__       (				self				:	Tuple,						__A				:	torch.FloatTensor,						__A				:	int,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	Optional[torch.Generator] = None,						__A				:	bool = True,						):
     UpperCAmelCase			:				Union[str, Any]										=					timestep
     UpperCAmelCase			:				str										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     UpperCAmelCase			:				List[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				str										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				Any										=					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
     UpperCAmelCase			:				int										=					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       UpperCAmelCase			:				Tuple										=					torch.clamp(__A,						-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
     UpperCAmelCase			:				Optional[Any]										=					model_output.device
     UpperCAmelCase			:				Optional[Any]										=					randn_tensor(model_output.shape,						generator=__A,						device=__A,						dtype=model_output.dtype		)
     UpperCAmelCase			:				List[str]										=					self.eta * self._get_variance(__A		) ** 0.5

     UpperCAmelCase			:				int										=					0
     if t > 0 and self.eta > 0:
       UpperCAmelCase			:				List[str]										=					std_dev_t * noise

     # 6. compute "direction pointing to x_t" of formula (12)
     # from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				Any										=					(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
     UpperCAmelCase			:				str										=					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
     UpperCAmelCase			:				Union[str, Any]										=					(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
     UpperCAmelCase			:				Optional[Any]										=					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=__A,						pred_original_sample=__A		)



   def 			__magic_name__       (				self				:	Dict,						__A				:	Dict,						__A				:	Tuple,						__A				:	Tuple=None		):
     UpperCAmelCase			:				Optional[int]										=					self.config.num_train_timesteps // self.num_inference_steps

     for i in range(__A		):
       UpperCAmelCase			:				Optional[Any]										=					self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCAmelCase			:				List[str]										=					randn_tensor(sample.shape,						dtype=sample.dtype,						generator=__A		)
         UpperCAmelCase			:				Optional[int]										=					noise.to(sample.device		)
       else:
         UpperCAmelCase			:				Union[str, Any]										=					randn_tensor(sample.shape,						generator=__A,						device=sample.device,						dtype=sample.dtype		)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase			:				Optional[int]										=					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample



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





   def __len__(				self				:	str		):
     return self.config.num_train_timesteps





