

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowerCamelCase							:  Dict     =				[nn.LayerNorm]

_lowerCamelCase							:  Optional[int]     =				logging.get_logger(__name__)

_lowerCamelCase							:  int     =				version.parse(version.parse(torch.__version__).base_version)

_lowerCamelCase							:  Dict     =				parsed_torch_version_base >= version.parse("2.0")
_lowerCamelCase							:  Dict     =				parsed_torch_version_base >= version.parse("1.12")
_lowerCamelCase							:  List[str]     =				parsed_torch_version_base >= version.parse("1.11")
_lowerCamelCase							:  Any     =				parsed_torch_version_base < version.parse("1.11")

def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[str]							,					self				:     Union[str, Any]     )    ->		str:
  from torch import _softmax_backward_data

  if is_torch_less_than_1_11:
    return _softmax_backward_data(UpperCAmelCase							,					UpperCAmelCase							,					parent.dim							,					self     )
  else:
    return _softmax_backward_data(UpperCAmelCase							,					UpperCAmelCase							,					parent.dim							,					self.dtype     )

def      a__	(						UpperCAmelCase				:     nn.Linear							,					UpperCAmelCase				:     torch.LongTensor							,					UpperCAmelCase				:     int = 0     )    ->		nn.Linear:
  UpperCAmelCase			:				Optional[Any]										=					index.to(layer.weight.device     )
  UpperCAmelCase			:				Optional[int]										=					layer.weight.index_select(UpperCAmelCase							,					UpperCAmelCase     ).clone().detach()
  if layer.bias is not None:
    if dim == 1:
      UpperCAmelCase			:				List[str]										=					layer.bias.clone().detach()
    else:
      UpperCAmelCase			:				Optional[Any]										=					layer.bias[index].clone().detach()
  UpperCAmelCase			:				List[str]										=					list(layer.weight.size()     )
  UpperCAmelCase			:				Any										=					len(UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					nn.Linear(new_size[1]							,					new_size[0]							,					bias=layer.bias is not None     ).to(layer.weight.device     )
  UpperCAmelCase			:				Dict										=					False
  new_layer.weight.copy_(W.contiguous()     )
  UpperCAmelCase			:				Any										=					True
  if layer.bias is not None:
    UpperCAmelCase			:				int										=					False
    new_layer.bias.copy_(b.contiguous()     )
    UpperCAmelCase			:				str										=					True
  return new_layer





class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Dict,						__A				:	Dict,						__A				:	Any		):
     super().__init__()
     UpperCAmelCase			:				str										=					nf
     UpperCAmelCase			:				str										=					nn.Parameter(torch.empty(__A,						__A		)		)
     UpperCAmelCase			:				Optional[int]										=					nn.Parameter(torch.zeros(__A		)		)
     nn.init.normal_(self.weight,						std=0.0_2		)





   def 			__magic_name__       (				self				:	Tuple,						__A				:	str		):
     UpperCAmelCase			:				List[Any]										=					x.size()[:-1] + (self.nf,)
     UpperCAmelCase			:				Any										=					torch.addmm(self.bias,						x.view(-1,						x.size(-1		)		),						self.weight		)
     UpperCAmelCase			:				str										=					x.view(__A		)
     return x






def      a__	(						UpperCAmelCase				:     ConvaD							,					UpperCAmelCase				:     torch.LongTensor							,					UpperCAmelCase				:     int = 1     )    ->		ConvaD:
  UpperCAmelCase			:				Union[str, Any]										=					index.to(layer.weight.device     )
  UpperCAmelCase			:				str										=					layer.weight.index_select(UpperCAmelCase							,					UpperCAmelCase     ).clone().detach()
  if dim == 0:
    UpperCAmelCase			:				List[str]										=					layer.bias.clone().detach()
  else:
    UpperCAmelCase			:				Union[str, Any]										=					layer.bias[index].clone().detach()
  UpperCAmelCase			:				Union[str, Any]										=					list(layer.weight.size()     )
  UpperCAmelCase			:				str										=					len(UpperCAmelCase     )
  UpperCAmelCase			:				List[str]										=					ConvaD(new_size[1]							,					new_size[0]     ).to(layer.weight.device     )
  UpperCAmelCase			:				int										=					False
  new_layer.weight.copy_(W.contiguous()     )
  UpperCAmelCase			:				Union[str, Any]										=					True
  UpperCAmelCase			:				str										=					False
  new_layer.bias.copy_(b.contiguous()     )
  UpperCAmelCase			:				Optional[Any]										=					True
  return new_layer

def      a__	(						UpperCAmelCase				:     Union[nn.Linear, ConvaD]							,					UpperCAmelCase				:     torch.LongTensor							,					UpperCAmelCase				:     Optional[int] = None     )    ->		Union[nn.Linear, ConvaD]:
  if isinstance(UpperCAmelCase							,					nn.Linear     ):
    return prune_linear_layer(UpperCAmelCase							,					UpperCAmelCase							,					dim=0 if dim is None else dim     )
  elif isinstance(UpperCAmelCase							,					UpperCAmelCase     ):
    return prune_convad_layer(UpperCAmelCase							,					UpperCAmelCase							,					dim=1 if dim is None else dim     )
  else:
    raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def      a__	(						UpperCAmelCase				:     Callable[..., torch.Tensor]							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     int							,					*UpperCAmelCase				:     Tuple     )    ->		torch.Tensor:
  assert len(UpperCAmelCase     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

  # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
  UpperCAmelCase			:				List[Any]										=					len(inspect.signature(UpperCAmelCase     ).parameters     )
  if num_args_in_forward_chunk_fn != len(UpperCAmelCase     ):
    raise ValueError(
        f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCAmelCase     )} input '''
        '''tensors are given'''     )

  if chunk_size > 0:
    UpperCAmelCase			:				Tuple										=					input_tensors[0].shape[chunk_dim]
    for input_tensor in input_tensors:
      if input_tensor.shape[chunk_dim] != tensor_shape:
        raise ValueError(
            f'''All input tenors have to be of the same shape: {tensor_shape}, '''
            f'''found shape {input_tensor.shape[chunk_dim]}'''     )

    if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
      raise ValueError(
          f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
          f'''size {chunk_size}'''     )

    UpperCAmelCase			:				Union[str, Any]										=					input_tensors[0].shape[chunk_dim] // chunk_size

    # chunk input tensor into tuples
    UpperCAmelCase			:				Dict										=					tuple(input_tensor.chunk(UpperCAmelCase							,					dim=UpperCAmelCase     ) for input_tensor in input_tensors     )
    # apply forward fn to every tuple
    UpperCAmelCase			:				Optional[int]										=					tuple(forward_fn(*UpperCAmelCase     ) for input_tensors_chunk in zip(*UpperCAmelCase     )     )
    # concatenate output at same dimension
    return torch.cat(UpperCAmelCase							,					dim=UpperCAmelCase     )

  return forward_fn(*UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     List[int]							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     Set[int]     )    ->		Tuple[Set[int], torch.LongTensor]:
  UpperCAmelCase			:				Dict										=					torch.ones(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					set(UpperCAmelCase     ) - already_pruned_heads  # Convert to set and remove already pruned heads
  for head in heads:
    # Compute how many pruned heads are before the head and move the index accordingly
    UpperCAmelCase			:				Union[str, Any]										=					head - sum(1 if h < head else 0 for h in already_pruned_heads     )
    UpperCAmelCase			:				List[Any]										=					0
  UpperCAmelCase			:				Any										=					mask.view(-1     ).contiguous().eq(1     )
  UpperCAmelCase			:				torch.LongTensor										=					torch.arange(len(UpperCAmelCase     )     )[mask].long()
  return heads, index

def      a__	(						*UpperCAmelCase				:     Union[torch.Tensor, List[torch.Tensor]]							,					UpperCAmelCase				:     Optional[str] = None     )    ->		Tuple[torch.Tensor, ...]:
  return torch.meshgrid(*UpperCAmelCase							,					indexing=UpperCAmelCase     )






def      a__	(						UpperCAmelCase				:     torch.Tensor     )    ->		Tuple[torch.device, int, int]:
  return tensor.device, storage_ptr(UpperCAmelCase     ), storage_size(UpperCAmelCase     )








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)





