import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			List[str]=0.999					,	__UpperCamelCase       :			str="cosine"					,	)				->   Optional[Any]:







  """simple docstring"""





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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







class __snake_case							(		__snake_case  ,						__snake_case						):
   lowerCAmelCase_								=				[e.name for e in KarrasDiffusionSchedulers]
   lowerCAmelCase_								=				2




   @register_to_config
   def __init__(       self     :							Optional[int]			,      _lowercase     :							List[Any] = 10_00			,      _lowercase     :							List[str] = 0.0_00_85			,      _lowercase     :							Union[str, Any] = 0.0_12			,      _lowercase     :							Optional[int] = "linear"			,      _lowercase     :							Any = None			,      _lowercase     :							Union[str, Any] = "epsilon"			,      _lowercase     :							Union[str, Any] = "linspace"			,      _lowercase     :							Optional[Any] = 0			,      ):



     """simple docstring"""






     if trained_betas is not None:
       SCREAMING_SNAKE_CASE__     				= torch.tensor(__UpperCamelCase			,      dtype=torch.floataa )
     elif beta_schedule == "linear":
       SCREAMING_SNAKE_CASE__     				= torch.linspace(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      dtype=torch.floataa )
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       SCREAMING_SNAKE_CASE__     				= (
           torch.linspace(beta_start**0.5			,      beta_end**0.5			,      __UpperCamelCase			,      dtype=torch.floataa ) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       SCREAMING_SNAKE_CASE__     				= betas_for_alpha_bar(__UpperCamelCase )
     else:
       raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

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

     #  set all values
     self.set_timesteps(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase )




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict			,      _lowercase     :							List[Any]=None ):



     """simple docstring"""






     if schedule_timesteps is None:
       SCREAMING_SNAKE_CASE__     				= self.timesteps

     SCREAMING_SNAKE_CASE__     				= (schedule_timesteps == timestep).nonzero()

     # The sigma index that is taken for the **very** first `step`
     # is always the second index (or the last index if there is only 1)
     # This way we can ensure we don't accidentally skip a sigma in
     # case we start in the middle of the denoising schedule (e.g. for image-to-image)
     if len(self._index_counter ) == 0:
       SCREAMING_SNAKE_CASE__     				= 1 if len(__UpperCamelCase ) > 1 else 0
     else:
       SCREAMING_SNAKE_CASE__     				= timestep.cpu().item() if torch.is_tensor(__UpperCamelCase ) else timestep
       SCREAMING_SNAKE_CASE__     				= self._index_counter[timestep_int]

     return indices[pos].item()




   @property
   def       __a		(       self     :							Any ):



     """simple docstring"""






     if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Optional[int]			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.index_for_timestep(__UpperCamelCase )

     if self.state_in_first_order:
       SCREAMING_SNAKE_CASE__     				= self.sigmas[step_index]
     else:
       SCREAMING_SNAKE_CASE__     				= self.sigmas_interpol[step_index - 1]

     SCREAMING_SNAKE_CASE__     				= sample / ((sigma**2 + 1) ** 0.5)
     return sample




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							str			,      _lowercase     :							List[str] = None			,      _lowercase     :							Union[str, Any] = None			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= num_inference_steps

     SCREAMING_SNAKE_CASE__     				= num_train_timesteps or self.config.num_train_timesteps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
       SCREAMING_SNAKE_CASE__     				= np.linspace(0			,      num_train_timesteps - 1			,      __UpperCamelCase			,      dtype=__UpperCamelCase )[::-1].copy()
     elif self.config.timestep_spacing == "leading":
       SCREAMING_SNAKE_CASE__     				= num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE__     				= (np.arange(0			,      __UpperCamelCase ) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase )
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       SCREAMING_SNAKE_CASE__     				= num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE__     				= (np.arange(__UpperCamelCase			,      0			,      -step_ratio )).round().copy().astype(__UpperCamelCase )
       timesteps -= 1
     else:
       raise ValueError(
           f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.""" )

     SCREAMING_SNAKE_CASE__     				= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
     SCREAMING_SNAKE_CASE__     				= torch.from_numpy(np.log(__UpperCamelCase ) ).to(__UpperCamelCase )

     SCREAMING_SNAKE_CASE__     				= np.interp(__UpperCamelCase			,      np.arange(0			,      len(__UpperCamelCase ) )			,      __UpperCamelCase )
     SCREAMING_SNAKE_CASE__     				= np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
     SCREAMING_SNAKE_CASE__     				= torch.from_numpy(__UpperCamelCase ).to(device=__UpperCamelCase )

     # compute up and down sigmas
     SCREAMING_SNAKE_CASE__     				= sigmas.roll(-1 )
     SCREAMING_SNAKE_CASE__     				= 0.0
     SCREAMING_SNAKE_CASE__     				= (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
     SCREAMING_SNAKE_CASE__     				= (sigmas_next**2 - sigmas_up**2) ** 0.5
     SCREAMING_SNAKE_CASE__     				= 0.0

     # compute interpolated sigmas
     SCREAMING_SNAKE_CASE__     				= sigmas.log().lerp(sigmas_down.log()			,      0.5 ).exp()
     SCREAMING_SNAKE_CASE__     				= 0.0

     # set sigmas
     SCREAMING_SNAKE_CASE__     				= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
     SCREAMING_SNAKE_CASE__     				= torch.cat(
         [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
     SCREAMING_SNAKE_CASE__     				= torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
     SCREAMING_SNAKE_CASE__     				= torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

     if str(__UpperCamelCase ).startswith("""mps""" ):
       # mps does not support float64
       SCREAMING_SNAKE_CASE__     				= torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase			,      dtype=torch.floataa )
     else:
       SCREAMING_SNAKE_CASE__     				= torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )

     SCREAMING_SNAKE_CASE__     				= self.sigma_to_t(__UpperCamelCase ).to(__UpperCamelCase			,      dtype=timesteps.dtype )
     SCREAMING_SNAKE_CASE__     				= torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])			,      dim=-1 ).flatten()

     SCREAMING_SNAKE_CASE__     				= torch.cat([timesteps[:1], interleaved_timesteps] )

     SCREAMING_SNAKE_CASE__     				= None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     SCREAMING_SNAKE_CASE__     				= defaultdict(__UpperCamelCase )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= sigma.log()

     # get distribution
     SCREAMING_SNAKE_CASE__     				= log_sigma - self.log_sigmas[:, None]

     # get sigmas range
     SCREAMING_SNAKE_CASE__     				= dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
     SCREAMING_SNAKE_CASE__     				= low_idx + 1

     SCREAMING_SNAKE_CASE__     				= self.log_sigmas[low_idx]
     SCREAMING_SNAKE_CASE__     				= self.log_sigmas[high_idx]

     # interpolate sigmas
     SCREAMING_SNAKE_CASE__     				= (low - log_sigma) / (low - high)
     SCREAMING_SNAKE_CASE__     				= w.clamp(0			,      1 )

     # transform interpolation to time range
     SCREAMING_SNAKE_CASE__     				= (1 - w) * low_idx + w * high_idx
     SCREAMING_SNAKE_CASE__     				= t.view(sigma.shape )
     return t




   @property
   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     return self.sample is None




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							Tuple			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							str = None			,      _lowercase     :							Dict = True			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.index_for_timestep(__UpperCamelCase )

     # advance index counter by 1
     SCREAMING_SNAKE_CASE__     				= timestep.cpu().item() if torch.is_tensor(__UpperCamelCase ) else timestep
     self._index_counter[timestep_int] += 1

     if self.state_in_first_order:
       SCREAMING_SNAKE_CASE__     				= self.sigmas[step_index]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_interpol[step_index]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_up[step_index]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_down[step_index - 1]
     else:
       # 2nd order / KPDM2's method
       SCREAMING_SNAKE_CASE__     				= self.sigmas[step_index - 1]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_interpol[step_index - 1]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_up[step_index - 1]
       SCREAMING_SNAKE_CASE__     				= self.sigmas_down[step_index - 1]

     # currently only gamma=0 is supported. This usually works best anyways.
     # We can support gamma in the future but then need to scale the timestep before
     # passing it to the model which requires a change in API
     SCREAMING_SNAKE_CASE__     				= 0
     SCREAMING_SNAKE_CASE__     				= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

     SCREAMING_SNAKE_CASE__     				= model_output.device
     SCREAMING_SNAKE_CASE__     				= randn_tensor(model_output.shape			,      dtype=model_output.dtype			,      device=__UpperCamelCase			,      generator=__UpperCamelCase )

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
       SCREAMING_SNAKE_CASE__     				= sigma_hat if self.state_in_first_order else sigma_interpol
       SCREAMING_SNAKE_CASE__     				= sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
       SCREAMING_SNAKE_CASE__     				= sigma_hat if self.state_in_first_order else sigma_interpol
       SCREAMING_SNAKE_CASE__     				= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
     elif self.config.prediction_type == "sample":
       raise NotImplementedError("""prediction_type not implemented yet: sample""" )
     else:
       raise ValueError(
           f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )

     if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       SCREAMING_SNAKE_CASE__     				= (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       SCREAMING_SNAKE_CASE__     				= sigma_interpol - sigma_hat

       # store for 2nd order step
       SCREAMING_SNAKE_CASE__     				= sample
       SCREAMING_SNAKE_CASE__     				= dt
       SCREAMING_SNAKE_CASE__     				= sample + derivative * dt
     else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       SCREAMING_SNAKE_CASE__     				= (sample - pred_original_sample) / sigma_interpol
       # 3. delta timestep
       SCREAMING_SNAKE_CASE__     				= sigma_down - sigma_hat

       SCREAMING_SNAKE_CASE__     				= self.sample
       SCREAMING_SNAKE_CASE__     				= None

       SCREAMING_SNAKE_CASE__     				= sample + derivative * dt
       SCREAMING_SNAKE_CASE__     				= prev_sample + noise * sigma_up

     if not return_dict:
       return (prev_sample,)

     return SchedulerOutput(prev_sample=__UpperCamelCase )




   def       __a		(       self     :							List[str]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict			,      _lowercase     :							Optional[Any]			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.sigmas.to(device=original_samples.device			,      dtype=original_samples.dtype )
     if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase ):
       # mps does not support float64
       SCREAMING_SNAKE_CASE__     				= self.timesteps.to(original_samples.device			,      dtype=torch.floataa )
       SCREAMING_SNAKE_CASE__     				= timesteps.to(original_samples.device			,      dtype=torch.floataa )
     else:
       SCREAMING_SNAKE_CASE__     				= self.timesteps.to(original_samples.device )
       SCREAMING_SNAKE_CASE__     				= timesteps.to(original_samples.device )

     SCREAMING_SNAKE_CASE__     				= [self.index_for_timestep(__UpperCamelCase			,      __UpperCamelCase ) for t in timesteps]

     SCREAMING_SNAKE_CASE__     				= sigmas[step_indices].flatten()
     while len(sigma.shape ) < len(original_samples.shape ):
       SCREAMING_SNAKE_CASE__     				= sigma.unsqueeze(-1 )

     SCREAMING_SNAKE_CASE__     				= original_samples + noise * sigma
     return noisy_samples




   def __len__(       self     :							Union[str, Any] ):



     """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
__magic_name__  :  Dict     			=   False
__magic_name__  :  Union[str, Any]     			=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__magic_name__  :  Optional[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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase=sys.argv[1:]					):
      A_								=					None
      A_								=					None
      A_								=					None
      A_								=					1
      A_	,	A_								=					getopt.getopt(__UpperCamelCase						,    "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(__UpperCamelCase					)
          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(__UpperCamelCase					), f'{outputfile}'
      if subset_list is not None:
          with open(os.path.realpath(__UpperCamelCase					)					) as f:
              A_								=					{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
      else:
          A_								=					None

      A_								=					CONFIG
      if torch.cuda.is_available():
          A_								=					"cuda"
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					os.path.realpath(__UpperCamelCase					)
      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 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					int(file.split("."					)[0].split("_"					)[-1]					)
      A_								=					os.path.join(self.inputdir						,    __UpperCamelCase					)
      return (img_id, filepath)

  @property
  def 					lowercase_						(					self					):
      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(__UpperCamelCase					)					)
          if len(__UpperCamelCase					) == self.batch:
              A_								=					batch
              A_								=					[]
              yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

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

  def __call__(					self					):
      # 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(__UpperCamelCase					)
          A_								=					self.model(
              __UpperCamelCase						,    __UpperCamelCase						,    scales_yx=__UpperCamelCase						,    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(__UpperCamelCase					)
              A_								=					self.schema.encode_batch(__UpperCamelCase					)
              writer.write_batch(__UpperCamelCase					)
          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        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    try:
        A_								=					json.load(snake_case__ )
        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__":
     __magic_name__  :  List[str]     			=   Extract(sys.argv[1:])
     extract()
     if not TEST:
          __magic_name__  :  List[Any]     			=   datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)






