import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
 import torch

 from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
 import pyanvml.pyanvml as nvml


UpperCAmelCase      		=					logging.get_logger(__name__)




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						PyTorchBenchmarkArguments
 _lowerCamelCase    :						PretrainedConfig
 _lowerCamelCase    :						str					     =			"PyTorch"

 @property
 def        __snake_case       (      self							:		Any	)			->				Any:
        return torch.__version__

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				float:
        _UpperCamelCase  :							Tuple	    =	self._prepare_inference_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_speed(_inference	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				[Memory, Optional[MemorySummary]]:
        _UpperCamelCase  :							Dict	    =	self._prepare_inference_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_memory(_inference	)

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				float:
        _UpperCamelCase  :							Optional[Any]	    =	self._prepare_train_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_speed(_train	)

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				[Memory, Optional[MemorySummary]]:
        _UpperCamelCase  :							Union[str, Any]	    =	self._prepare_train_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_memory(_train	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				Callable[[], None]:
        _UpperCamelCase  :							List[str]	    =	self.config_dict[model_name]

        if self.args.torchscript:
               _UpperCamelCase  :							int	    =	True

        _UpperCamelCase  :							int	    =	(
            hasattr(lowerCamelCase							,      'architectures'	)
            and isinstance(config.architectures							,      lowerCamelCase	)
            and len(config.architectures	) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      _UpperCamelCase  :							List[str]	    =	config.architectures[0]
                      _UpperCamelCase  :							Union[str, Any]	    =	__import__('transformers'							,      fromlist=[model_class]	)
                      _UpperCamelCase  :							Dict	    =	getattr(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							List[str]	    =	model_cls(lowerCamelCase	)
               except ImportError:
                      raise ImportError(
                          f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                          ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'	)
        else:
               _UpperCamelCase  :							List[str]	    =	MODEL_MAPPING[config.__class__](lowerCamelCase	)

        model.eval()
        model.to(self.args.device	)

        # encoder-decoder has vocab size saved differently
        _UpperCamelCase  :							Any	    =	config.vocab_size if hasattr(lowerCamelCase							,      'vocab_size'	) else config.encoder.vocab_size
        _UpperCamelCase  :							List[str]	    =	torch.randint(lowerCamelCase							,      (batch_size, sequence_length)							,      dtype=torch.long							,      device=self.args.device	)

        if self.args.fpaa:
               logger.info('Running training in Mixed Precision...'	)
               if not self.args.is_gpu:
                      raise ValueError('Mixed precision is possible only for GPU.'	)
               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        if self.args.torchscript:
               with torch.no_grad():
                      _UpperCamelCase  :							str	    =	torch.jit.trace(lowerCamelCase							,      lowerCamelCase	)
        else:
               _UpperCamelCase  :							Union[str, Any]	    =	model

        def encoder_decoder_forward():
               with torch.no_grad():
                      _UpperCamelCase  :							Tuple	    =	inference_model(lowerCamelCase							,      decoder_input_ids=lowerCamelCase	)
               return outputs

        def encoder_forward():
               with torch.no_grad():
                      _UpperCamelCase  :							Tuple	    =	inference_model(lowerCamelCase	)
               return outputs

        _UpperCamelCase  :							int	    =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				Callable[[], None]:
        _UpperCamelCase  :							int	    =	self.config_dict[model_name]

        _UpperCamelCase  :							int	    =	(
            hasattr(lowerCamelCase							,      'architectures'	)
            and isinstance(config.architectures							,      lowerCamelCase	)
            and len(config.architectures	) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      _UpperCamelCase  :							Tuple	    =	config.architectures[0]
                      _UpperCamelCase  :							Optional[int]	    =	__import__('transformers'							,      fromlist=[model_class]	)
                      _UpperCamelCase  :							Any	    =	getattr(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							int	    =	model_cls(lowerCamelCase	)
               except ImportError:
                      raise ImportError(
                          f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                          ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'	)
        else:
               _UpperCamelCase  :							int	    =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

        if self.args.torchscript:
               raise NotImplementedError('Training for torchscript is currently not implemented'	)
        else:
               _UpperCamelCase  :							List[str]	    =	model

        model.train()
        model.to(self.args.device	)

        # encoder-decoder has vocab size saved differently
        _UpperCamelCase  :							Optional[Any]	    =	config.vocab_size if hasattr(lowerCamelCase							,      'vocab_size'	) else config.encoder.vocab_size
        _UpperCamelCase  :							Union[str, Any]	    =	torch.randint(lowerCamelCase							,      (batch_size, sequence_length)							,      dtype=torch.long							,      device=self.args.device	)

        if self.args.fpaa:
               logger.info('Running training in Mixed Precision...'	)
               if not self.args.is_gpu:
                      raise ValueError('Mixed precision is possible only for GPU.'	)

               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        def compute_loss_and_backprob_encoder():
               _UpperCamelCase  :							List[str]	    =	train_model(lowerCamelCase							,      labels=lowerCamelCase	)[0]
               loss.backward()
               return loss

        def compute_loss_and_backprob_encoder_decoder():
               _UpperCamelCase  :							List[Any]	    =	train_model(lowerCamelCase							,      decoder_input_ids=lowerCamelCase							,      labels=lowerCamelCase	)[0]
               loss.backward()
               return loss

        _UpperCamelCase  :							Optional[int]	    =	(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		List[Any]	)			->				float:
        try:
               if self.args.is_tpu or self.args.torchscript:
                      # run additional 10 times to stabilize compilation for tpu and torchscript
                      logger.info('Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'	)
                      timeit.repeat(
                          lowerCamelCase							,      repeat=1							,      number=5							,      )

               # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
               _UpperCamelCase  :							int	    =	timeit.repeat(
                   lowerCamelCase							,      repeat=self.args.repeat							,      number=1_0							,      )

               if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                      import torch_xla.debug.metrics as met

                      self.print_fn(met.metrics_report()	)

               return min(lowerCamelCase	) / 1_0.0
        except RuntimeError as e:
               self.print_fn(f"Doesn't fit on GPU. {e}"	)
               return "N/A"

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		Callable[[], None]	)			->				[Memory, MemorySummary]:
        try:
               if self.args.trace_memory_line_by_line:
                      _UpperCamelCase  :							List[str]	    =	start_memory_tracing('transformers'	)

               if self.args.is_tpu:
                      # tpu
                      raise NotImplementedError(
                          'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'
                          ' `--no-memory` or `args.memory=False`'	)
               elif self.args.is_gpu:
                      if not is_pyanvml_available():
                             logger.warning(
                                 'py3nvml not installed, we won\'t log GPU memory usage. '
                                 'Install py3nvml (pip install py3nvml) to log information about GPU.'	)
                             _UpperCamelCase  :							int	    =	'N/A'
                      else:
                             logger.info(
                                 'Measuring total GPU usage on GPU device. Make sure to not have additional processes running'
                                 ' on the same GPU.'	)
                             # init nvml
                             nvml.nvmlInit()
                             func()
                             _UpperCamelCase  :							List[str]	    =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
                             _UpperCamelCase  :							List[Any]	    =	nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
                             _UpperCamelCase  :							Dict	    =	meminfo.used
                             _UpperCamelCase  :							str	    =	Memory(lowerCamelCase	)
                             # shutdown nvml
                             nvml.nvmlShutdown()
               else:
                      # cpu
                      _UpperCamelCase  :							str	    =	measure_peak_memory_cpu(lowerCamelCase	)
                      _UpperCamelCase  :							List[str]	    =	Memory(lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else memory_bytes

               if self.args.trace_memory_line_by_line:
                      _UpperCamelCase  :							Optional[int]	    =	stop_memory_tracing(lowerCamelCase	)
               else:
                      _UpperCamelCase  :							int	    =	None

               return memory, summary
        except RuntimeError as e:
               self.print_fn(f"Doesn't fit on GPU. {e}"	)
               return "N/A", None




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
UpperCAmelCase      		=					False
UpperCAmelCase      		=					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase      		=					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  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      '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  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	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:
                      _UpperCamelCase  :							str	    =	{self._vqa_file_split()[0] for x in tryload(lowerCamelCase	)}
        else:
               _UpperCamelCase  :							Any	    =	None

        _UpperCamelCase  :							Any	    =	CONFIG
        if torch.cuda.is_available():
               _UpperCamelCase  :							List[str]	    =	'cuda'
        _UpperCamelCase  :							int	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	os.path.realpath(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	Preprocess(self.config	)
        _UpperCamelCase  :							Tuple	    =	GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'							,      config=self.config	)
        _UpperCamelCase  :							List[Any]	    =	batch_size if batch_size != 0 else 1
        _UpperCamelCase  :							Any	    =	DEFAULT_SCHEMA

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

 def __call__(      self							:		Optional[Any]	)			->				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  :							Any	    =	self.preprocess(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.model(
                   lowerCamelCase							,      lowerCamelCase							,      scales_yx=lowerCamelCase							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
               _UpperCamelCase  :							Tuple	    =	output_dict.pop('normalized_boxes'	)
               if not TEST:
                      _UpperCamelCase  :							Dict	    =	np.array(lowerCamelCase	)
                      _UpperCamelCase  :							Optional[int]	    =	self.schema.encode_batch(lowerCamelCase	)
                      writer.write_batch(lowerCamelCase	)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	stream.read().split('\n'					)
       return data


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



