




import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
      import torch_xla.core.xla_model as xm  # noqa: F401

      __SCREAMING_SNAKE_CASE    :   List[str]       =True
except ImportError:
      __SCREAMING_SNAKE_CASE    :   List[Any]       =False


# Cache this result has it's a C FFI call which can be pretty time-consuming
__SCREAMING_SNAKE_CASE    :   str       =torch.distributed.is_available()

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				importlib.util.find_spec(lowerCamelCase						) is not None
      if package_exists:
            try:
                  lowerCamelCase__        =				importlib.metadata.metadata(lowerCamelCase						)
                  return True
            except importlib.metadata.PackageNotFoundError:
                  return False

def 			lowerCAmelCase_			(  )					->			bool:


      '''simple docstring'''




      return _torch_distributed_available

def 			lowerCAmelCase_			(  )					->			Optional[Any]:


      '''simple docstring'''




      try:
            pass
      except ImportError:
            print(
                'Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'
                ' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'
                ' Bindings for PyTorch*.'						)
      return (
          importlib.util.find_spec('torch_ccl'						) is not None
          or importlib.util.find_spec('oneccl_bindings_for_pytorch'						) is not None
      )

def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      return importlib.metadata.version('oneccl_bind_pt'						)

def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('transformer_engine'						)

@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       Any=True						)					->			Dict:


      '''simple docstring'''




      if torch.cuda.is_available():
            return False
      if _tpu_available and check_device:
            try:
                  # Will raise a RuntimeError if no XLA configuration is found
                  lowerCamelCase__        =				xm.xla_device()
                  return True
            except RuntimeError:
                  return False
      return _tpu_available

def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      return _is_package_available('deepspeed'						)

def 			lowerCAmelCase_			(  lowerCamelCase:       Dict=False						)					->			Union[str, Any]:


      '''simple docstring'''




      if is_tpu_available():
            return not ignore_tpu
      if torch.cuda.is_available():
            return torch.cuda.is_bfaa_supported()
      if is_npu_available():
            return False
      return True

def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				_is_package_available('bitsandbytes'						)
      if package_exists:
            lowerCamelCase__        =				version.parse(importlib.metadata.version('bitsandbytes'						)						)
            return compare_versions(lowerCamelCase     ,				'>='     ,				'0.39.0'						)
      return False

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				_is_package_available('bitsandbytes'						)
      if package_exists:
            lowerCamelCase__        =				version.parse(importlib.metadata.version('bitsandbytes'						)						)
            return compare_versions(lowerCamelCase     ,				'>='     ,				'0.37.2'						)
      return False

def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('bitsandbytes'						)

def 			lowerCAmelCase_			(  )					->			str:


      '''simple docstring'''




      if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'     ,				'False'						)						) == 1:
            lowerCamelCase__        =				importlib.util.find_spec('megatron'						) is not None
            if package_exists:
                  try:
                        lowerCamelCase__        =				parse(importlib.metadata.version('megatron-lm'						)						)
                        return compare_versions(lowerCamelCase     ,				'>='     ,				'2.2.0'						)
                  except Exception as e:
                        warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""						)
                        return False

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      return _is_package_available('safetensors'						)

def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      return _is_package_available('transformers'						)

def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      return _is_package_available('datasets'						)

def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return _is_package_available('aim'						)

def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      return _is_package_available('tensorboard'						) or _is_package_available('tensorboardX'						)

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      return _is_package_available('wandb'						)

def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      return _is_package_available('comet_ml'						)

def 			lowerCAmelCase_			(  )					->			Dict:


      '''simple docstring'''




      return _is_package_available('boto3'						)

def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      if _is_package_available('rich'						):
            if "ACCELERATE_DISABLE_RICH" in os.environ:
                  warnings.warn(
                      '`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'						)
                  return not parse_flag_from_env('ACCELERATE_DISABLE_RICH'     ,				lowerCamelCase						)
            return parse_flag_from_env('ACCELERATE_ENABLE_RICH'     ,				lowerCamelCase						)
      return False

def 			lowerCAmelCase_			(  )					->			Tuple:


      '''simple docstring'''




      return _is_package_available('sagemaker'						)

def 			lowerCAmelCase_			(  )					->			int:


      '''simple docstring'''




      return _is_package_available('tqdm'						)

def 			lowerCAmelCase_			(  )					->			Tuple:


      '''simple docstring'''




      return _is_package_available('mlflow'						)

def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




      return is_torch_version('>='     ,				'1.12'						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()

def 			lowerCAmelCase_			(  )					->			Dict:


      '''simple docstring'''




      def get_major_and_minor_from_version(lowerCamelCase:       List[str]						):
            return str(version.parse(lowerCamelCase						).major						) + "." + str(version.parse(lowerCamelCase						).minor						)

      lowerCamelCase__        =				importlib.metadata.version('torch'						)
      if importlib.util.find_spec('intel_extension_for_pytorch'						) is None:
            return False
      lowerCamelCase__        =				'N/A'
      try:
            lowerCamelCase__        =				importlib.metadata.version('intel_extension_for_pytorch'						)
      except importlib.metadata.PackageNotFoundError:
            return False
      lowerCamelCase__        =				get_major_and_minor_from_version(_torch_version						)
      lowerCamelCase__        =				get_major_and_minor_from_version(_ipex_version						)
      if torch_major_and_minor != ipex_major_and_minor:
            warnings.warn(
                f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
            return False
      return True

@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       int=False						)					->			str:


      '''simple docstring'''




      if importlib.util.find_spec('torch'						) is None or importlib.util.find_spec('torch_npu'						) is None:
            return False

      import torch
      import torch_npu  # noqa: F401

      if check_device:
            try:
                  # Will raise a RuntimeError if no NPU is found
                  lowerCamelCase__        =				torch.npu.device_count()
                  return torch.npu.is_available()
            except RuntimeError:
                  return False
      return hasattr(lowerCamelCase     ,				'npu'						) and torch.npu.is_available()


@lru_cache
def 			lowerCAmelCase_			(  lowerCamelCase:       Any=False						)					->			List[Any]:


      '''simple docstring'''




      if not parse_flag_from_env('ACCELERATE_USE_XPU'     ,				default=lowerCamelCase						):
            return False
      if is_ipex_available():
            import torch

            if is_torch_version('<='     ,				'1.12'						):
                  return False
      else:
            return False

      import intel_extension_for_pytorch  # noqa: F401

      if check_device:
            try:
                  # Will raise a RuntimeError if no XPU  is found
                  lowerCamelCase__        =				torch.xpu.device_count()
                  return torch.xpu.is_available()
            except RuntimeError:
                  return False
      return hasattr(lowerCamelCase     ,				'xpu'						) and torch.xpu.is_available()





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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   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_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				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:
                       lowerCamelCase__        =				{self._vqa_file_split()[0] for x in tryload(A__			)}
           else:
                 lowerCamelCase__        =				None

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




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            try:
                  lowerCamelCase__        =				list(data.keys()						)
            except Exception:
                  lowerCamelCase__        =				[d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__        =				eval(stream.read()						)
            except Exception:
                  lowerCamelCase__        =				stream.read().split('\n'						)
      return data


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