"""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 					a					:



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

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

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



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Dict     )      -> str:
      __a			      =						int(file.split("""."""     )[0].split("""_"""     )[-1]     )
      __a			      =						os.path.join(self.inputdir						,  lowerCamelCase_     )
      return (img_id, filepath)



  @property
  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> str:
      __a			      =						[]
      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(lowerCamelCase_     )     )
          if len(lowerCamelCase_     ) == self.batch:
              __a			      =						batch
              __a			      =						[]
              yield list(map(lowerCamelCase_						,  zip(*lowerCamelCase_     )     )     )

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



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

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]						):
    try:
        __a			      =						json.load(_lowerCAmelCase						)
        try:
            __a			      =						list(data.keys()						)
        except Exception:
            __a			      =						[d["""img_id"""] for d in data]
    except Exception:
        try:
            __a			      =						eval(stream.read()						)
        except Exception:
            __a			      =						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)

"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 					a					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

  # setable values
  A_ :				jnp.ndarray
  A_ :				jnp.ndarray
  A_ :				Optional[int] 			=							None



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






class 					a					(				A_   ,		A_							):
  A_ :				Optional[int] 			=							[e.name for e in FlaxKarrasDiffusionSchedulers]

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      if common is None:
          __a			      =						CommonSchedulerState.create(self     )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __a			      =						(
          jnp.array(1.0						,  dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

      # standard deviation of the initial noise distribution
      __a			      =						jnp.array(1.0						,  dtype=self.dtype     )

      __a			      =						jnp.arange(0						,  self.config.num_train_timesteps     ).round()[::-1]

      return DDIMSchedulerState.create(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    Optional[int] = None     )      -> jnp.ndarray:
      return sample



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __a			      =						self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      __a			      =						(jnp.arange(0						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

      return state.replace(
          num_inference_steps=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						state.common.alphas_cumprod[timestep]
      __a			      =						jnp.where(
          prev_timestep >= 0						,  state.common.alphas_cumprod[prev_timestep]						,  state.final_alpha_cumprod     )
      __a			      =						1 - alpha_prod_t
      __a			      =						1 - alpha_prod_t_prev

      __a			      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    bool = True						,  )      -> Union[FlaxDDIMSchedulerOutput, Tuple]:
      if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""     )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __a			      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

      __a			      =						state.common.alphas_cumprod
      __a			      =						state.final_alpha_cumprod

      # 2. compute alphas, betas
      __a			      =						alphas_cumprod[timestep]
      __a			      =						jnp.where(prev_timestep >= 0						,  alphas_cumprod[prev_timestep]						,  lowerCamelCase_     )

      __a			      =						1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          __a			      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          __a			      =						model_output
      elif self.config.prediction_type == "sample":
          __a			      =						model_output
          __a			      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          __a			      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          __a			      =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              """ `v_prediction`"""     )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __a			      =						self._get_variance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						eta * variance ** 0.5

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

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps
