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     =     False
_SCREAMING_SNAKE_CASE     =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_SCREAMING_SNAKE_CASE     =     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, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)







class        UpperCAmelCase_					:




    def __init__(       self	: Union[str, Any]   ,   lowerCAmelCase	: Optional[Any]=sys.argv[1:] )						->			List[str]:
      a__  		=  None
      a__  		=  None
      a__  		=  None
      a__  		=  1
      a__  		=  getopt.getopt(SCREAMING_SNAKE_CASE__   ,   "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(SCREAMING_SNAKE_CASE__ )
        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(SCREAMING_SNAKE_CASE__ ), f"{outputfile}"
      if subset_list is not None:
        with open(os.path.realpath(SCREAMING_SNAKE_CASE__ ) ) as f:
          a__  		=  {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__ )}
      else:
        a__  		=  None

      a__  		=  CONFIG
      if torch.cuda.is_available():
        a__  		=  """cuda"""
      a__  		=  os.path.realpath(SCREAMING_SNAKE_CASE__ )
      a__  		=  os.path.realpath(SCREAMING_SNAKE_CASE__ )
      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	: Dict   ,   lowerCAmelCase	: List[Any] )						->			List[str]:
      a__  		=  int(file.split("." )[0].split("_" )[-1] )
      a__  		=  os.path.join(self.inputdir   ,   SCREAMING_SNAKE_CASE__ )
      return (img_id, filepath)




    @property
    def     __lowerCamelCase			(       self	: List[Any] )						->			Optional[int]:
      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(SCREAMING_SNAKE_CASE__ ) )
        if len(SCREAMING_SNAKE_CASE__ ) == self.batch:
          a__  		=  batch
          a__  		=  []
          yield list(map(SCREAMING_SNAKE_CASE__   ,   zip(*SCREAMING_SNAKE_CASE__ ) ) )

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




    def __call__(       self	: Any )						->			List[str]:

      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__  		=  self.preprocess(SCREAMING_SNAKE_CASE__ )
        a__  		=  self.model(
            SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   scales_yx=SCREAMING_SNAKE_CASE__   ,   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(SCREAMING_SNAKE_CASE__ )
          a__  		=  self.schema.encode_batch(SCREAMING_SNAKE_CASE__ )
          writer.write_batch(SCREAMING_SNAKE_CASE__ )
        if TEST:
          break
        # finalizer the writer
      if not TEST:
        a__  		=  writer.finalize()
        print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb" )

def 					__lowercase						(    lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  try:
    a__  		=  json.load(A__       )
    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__":
     _SCREAMING_SNAKE_CASE     =     Extract(sys.argv[1:])
     extract()
     if not TEST:
          _SCREAMING_SNAKE_CASE     =     datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





