
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 __snake_case	(   lowerCAmelCase  ):
    _a	:		torch.FloatTensor
    _a	:		torch.FloatTensor







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=0.999      ,			SCREAMING_SNAKE_CASE__="cosine"      ,			)				->					Tuple:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__       ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

       elif alpha_transform_type == "exp":

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

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

       lowercase							:  int 			=   []
       for i in range(SCREAMING_SNAKE_CASE__       ):
              lowercase							:  Union[str, Any] 			=   i / num_diffusion_timesteps
              lowercase							:  List[str] 			=   (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__       ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__       )      ,			SCREAMING_SNAKE_CASE__       )       )
       return torch.tensor(SCREAMING_SNAKE_CASE__      ,			dtype=torch.floataa       )




class __snake_case	(   lowerCAmelCase      ,			lowerCAmelCase  ):
    _a	:		List[Any]=       1
    @register_to_config
    def __init__(						self	,snake_case = 1000	,snake_case = 0.0_001	,snake_case = 0.02	,snake_case = "linear"	,snake_case = 0.0	,snake_case = None	,snake_case = True	,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:  Union[str, Any] 			=   torch.from_numpy(snake_case   )
           elif beta_schedule == "linear":
                  lowercase							:  Any 			=   torch.linspace(snake_case	,snake_case	,snake_case	,dtype=torch.floataa   )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowercase							:  Union[str, Any] 			=   (
                      torch.linspace(beta_start**0.5	,beta_end**0.5	,snake_case	,dtype=torch.floataa   ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowercase							:  int 			=   betas_for_alpha_bar(snake_case   )
           elif beta_schedule == "sigmoid":
                  # GeoDiff sigmoid schedule
                  lowercase							:  Optional[Any] 			=   torch.linspace(-6	,6	,snake_case   )
                  lowercase							:  Union[str, Any] 			=   torch.sigmoid(snake_case   ) * (beta_end - beta_start) + beta_start
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

           lowercase							:  List[str] 			=   1.0 - self.betas
           lowercase							:  int 			=   torch.cumprod(self.alphas	,dim=0   )
           lowercase							:  List[str] 			=   torch.tensor(1.0   )

           lowercase							:  Any 			=   torch.tensor(1.0   )

           # standard deviation of the initial noise distribution
           lowercase							:  Dict 			=   1.0

           # setable values
           lowercase							:  Optional[int] 			=   None
           lowercase							:  Optional[Any] 			=   torch.from_numpy(np.arange(0	,snake_case   )[::-1].copy()   )

           lowercase							:  List[str] 			=   eta
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None   ):



           '''simple docstring'''







           return sample
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = 10	,snake_case = 10	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  Tuple 			=   min(self.config.num_train_timesteps	,snake_case   )
           lowercase							:  int 			=   num_inference_steps

           lowercase							:  int 			=   []

           lowercase							:  Optional[Any] 			=   {}
           for j in range(0	,num_inference_steps - jump_length	,snake_case   ):
                  lowercase							:  int 			=   jump_n_sample - 1

           lowercase							:  List[Any] 			=   num_inference_steps
           while t >= 1:
                  lowercase							:  Union[str, Any] 			=   t - 1
                  timesteps.append(snake_case   )

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

           lowercase							:  Tuple 			=   np.array(snake_case   ) * (self.config.num_train_timesteps // self.num_inference_steps)
           lowercase							:  str 			=   torch.from_numpy(snake_case   ).to(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  str 			=   t - self.config.num_train_timesteps // self.num_inference_steps

           lowercase							:  Tuple 			=   self.alphas_cumprod[t]
           lowercase							:  List[str] 			=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           lowercase							:  Tuple 			=   1 - alpha_prod_t
           lowercase							:  Optional[int] 			=   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							:  Optional[int] 			=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case = None	,snake_case = True	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   timestep
           lowercase							:  int 			=   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

           lowercase							:  Optional[Any] 			=   0
           if t > 0 and self.eta > 0:
                  lowercase							:  Optional[Any] 			=   std_dev_t * noise

           # 6. compute "direction pointing to x_t" of formula (12)
           # from https://arxiv.org/pdf/2010.02502.pdf
           lowercase							:  List[str] 			=   (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							:  Dict 			=   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							:  str 			=   (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							:  str 			=   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=snake_case	,pred_original_sample=snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.config.num_train_timesteps // self.num_inference_steps

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

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

           return sample
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,):



           '''simple docstring'''







           raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""   )
    def __len__(						self   ):



           '''simple docstring'''







           return self.config.num_train_timesteps


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
lowercase						:      List[Any]							=							False
lowercase						:      Union[str, Any]							=							Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase						:      List[str]							=							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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

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



           '''simple docstring'''







           lowercase							:  Dict 			=   None
           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Tuple 			=   None
           lowercase							:  Optional[int] 			=   1
           lowercase			,    lowercase							:  str 			=   getopt.getopt(snake_case	,"""i:o:b:s"""	,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]   )
           for opt, arg in opts:
                  if opt in ("-i", "--inputdir"):
                         lowercase							:  List[Any] 			=   arg
                  elif opt in ("-o", "--outfile"):
                         lowercase							:  Any 			=   arg
                  elif opt in ("-b", "--batch_size"):
                         lowercase							:  Any 			=   int(snake_case   )
                  elif opt in ("-s", "--subset_list"):
                         lowercase							:  str 			=   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:
                         lowercase							:  Dict 			=   {self._vqa_file_split()[0] for x in tryload(snake_case   )}
           else:
                  lowercase							:  Optional[Any] 			=   None

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



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   int(file.split("""."""   )[0].split("""_"""   )[-1]   )
           lowercase							:  List[str] 			=   os.path.join(self.inputdir	,snake_case   )
           return (img_id, filepath)
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
           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:
                         lowercase							:  Dict 			=   batch
                         lowercase							:  int 			=   []
                         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:
                  lowercase							:  Any 			=   datasets.ArrowWriter(features=self.schema	,path=self.outputfile   )
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
                  lowercase			,    lowercase			,    lowercase							:  Tuple 			=   self.preprocess(snake_case   )
                  lowercase							:  Dict 			=   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"""	,)
                  lowercase							:  int 			=   output_dict.pop("""normalized_boxes"""   )
                  if not TEST:
                         lowercase							:  Optional[int] 			=   np.array(snake_case   )
                         lowercase							:  Tuple 			=   self.schema.encode_batch(snake_case   )
                         writer.write_batch(snake_case   )
                  if TEST:
                         break
                  # finalizer the writer
           if not TEST:
                  lowercase			,    lowercase							:  Dict 			=   writer.finalize()
                  print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       try:
              lowercase							:  Dict 			=   json.load(SCREAMING_SNAKE_CASE__       )
              try:
                     lowercase							:  Tuple 			=   list(data.keys()       )
              except Exception:
                     lowercase							:  Tuple 			=   [d["""img_id"""] for d in data]
       except Exception:
              try:
                     lowercase							:  Optional[Any] 			=   eval(stream.read()       )
              except Exception:
                     lowercase							:  Any 			=   stream.read().split("""\n"""       )
       return data


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