


'''simple docstring'''





import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						__magic_name__				:							List[Any]				=      set()
						# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
						for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
												__magic_name__				:							Optional[int]				=      getattr(transformers.models.auto      ,						snake_case_      ,						snake_case_				)
												if module is None:
																		continue
												# all mappings in a single auto modeling file
												__magic_name__				:							Optional[int]				=      [
												    x
												    for x in dir(snake_case_				)
												    if x.endswith('_MAPPING_NAMES'				)
												    and (x.startswith('MODEL_'				) or x.startswith('TF_MODEL_'				) or x.startswith('FLAX_MODEL_'				))
												]
												for name in mapping_names:
																		__magic_name__				:							Any				=      getattr(snake_case_      ,						snake_case_				)
																		if mapping is not None:
																								for v in mapping.values():
																														if isinstance(snake_case_      ,						(list, tuple)				):
																																				model_names.update(snake_case_				)
																														elif isinstance(snake_case_      ,						snake_case_				):
																																				model_names.add(snake_case_				)

						return sorted(snake_case_				)
def  __SCREAMING_SNAKE_CASE       (				)       ->  Optional[Any]:
						# All model names defined in auto mappings
						__magic_name__				:							int				=      set(get_all_model_names()				)

						with open('tests/utils/tiny_model_summary.json'				) as fp:
												__magic_name__				:							Union[str, Any]				=      json.load(snake_case_				)
						__magic_name__				:							int				=      set()
						for model_base_name in tiny_model_info:
												tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']				)

						# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
						__magic_name__				:							Optional[Any]				=      model_names.difference(snake_case_				)
						for model_name in copy.copy(snake_case_				):
												if not model_name.startswith('TF'				) and f"TF{model_name}" in not_on_hub:
																		tiny_models_names.remove(snake_case_				)
												elif model_name.startswith('TF'				) and model_name[2:] in not_on_hub:
																		tiny_models_names.remove(snake_case_				)

						return sorted(snake_case_				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any				)       ->  Tuple:
						__magic_name__				:							Dict				=      COMPOSITE_MODELS.values()

						# All tiny model base names on Hub
						__magic_name__				:							Union[str, Any]				=      get_all_model_names()
						__magic_name__				:							Optional[Any]				=      hf_api.list_models(
						    filter=ModelFilter(
						        author='hf-internal-testing'      ,						)				)
						__magic_name__				:							str				=      set()
						for x in models:
												__magic_name__				:							List[Any]				=      x.modelId
												__magic_name__				:							List[str]				=      model.split('/'				)
												if not model.startswith('tiny-random-'				):
																		continue
												__magic_name__				:							str				=      model.replace('tiny-random-'      ,						''				)
												if not model[0].isupper():
																		continue
												if model not in model_names and model not in special_models:
																		continue
												_models.add(snake_case_				)

						__magic_name__				:							Dict				=      sorted(_models				)
						# All tiny model names on Hub
						__magic_name__				:							Optional[int]				=      {}
						for model in models:
												__magic_name__				:							Optional[int]				=      f"hf-internal-testing/tiny-random-{model}"
												__magic_name__				:							str				=      model.split('-'				)[0]
												try:
																		__magic_name__				:							int				=      hf_api.repo_info(snake_case_				)
																		__magic_name__				:							Optional[int]				=      {
																		    '''tokenizer_classes''': set(),
																		    '''processor_classes''': set(),
																		    '''model_classes''': set(),
																		    '''sha''': repo_info.sha,
																		}
												except Exception:
																		continue
												try:
																		time.sleep(1				)
																		__magic_name__				:							List[str]				=      AutoTokenizer.from_pretrained(snake_case_				)
																		content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__				)
												except Exception:
																		pass
												try:
																		time.sleep(1				)
																		__magic_name__				:							int				=      AutoTokenizer.from_pretrained(snake_case_      ,						use_fast=snake_case_				)
																		content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__				)
												except Exception:
																		pass
												try:
																		time.sleep(1				)
																		__magic_name__				:							int				=      AutoImageProcessor.from_pretrained(snake_case_				)
																		content["processor_classes"].add(img_p.__class__.__name__				)
												except Exception:
																		pass
												try:
																		time.sleep(1				)
																		__magic_name__				:							Optional[int]				=      AutoFeatureExtractor.from_pretrained(snake_case_				)
																		if not isinstance(snake_case_      ,						snake_case_				):
																								content["processor_classes"].add(feat_p.__class__.__name__				)
												except Exception:
																		pass
												try:
																		time.sleep(1				)
																		__magic_name__				:							Tuple				=      getattr(snake_case_      ,						snake_case_				)
																		__magic_name__				:							Tuple				=      model_class.from_pretrained(snake_case_				)
																		content["model_classes"].add(m.__class__.__name__				)
												except Exception:
																		pass
												try:
																		time.sleep(1				)
																		__magic_name__				:							int				=      getattr(snake_case_      ,						f"TF{model}"				)
																		__magic_name__				:							Union[str, Any]				=      model_class.from_pretrained(snake_case_				)
																		content["model_classes"].add(m.__class__.__name__				)
												except Exception:
																		pass

												__magic_name__				:							List[str]				=      sorted(content['tokenizer_classes']				)
												__magic_name__				:							Tuple				=      sorted(content['processor_classes']				)
												__magic_name__				:							Optional[Any]				=      sorted(content['model_classes']				)

												__magic_name__				:							List[Any]				=      content
												with open(os.path.join(snake_case_      ,						'hub_tiny_model_summary.json'				)      ,						'w'				) as fp:
																		json.dump(snake_case_      ,						snake_case_      ,						ensure_ascii=snake_case_      ,						indent=4				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
			snake_case__     							= parser.parse_args()

			# This has to be `spawn` to avoid hanging forever!
			multiprocessing.set_start_method('spawn')

			snake_case__     							= "tiny_models"
			snake_case__     							= True
			snake_case__     							= None
			snake_case__     							= get_tiny_model_names_from_repo()
			snake_case__     							= True
			snake_case__     							= True
			snake_case__     							= "hf-internal-testing"

			create_tiny_models(
			    output_path,
			    all,
			    model_types,
			    models_to_skip,
			    no_check,
			    upload,
			    organization,
			    token=os.environ.get('TOKEN', None),
			    num_workers=args.num_workers,
			)






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
snake_case__     							= False
snake_case__     							= Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
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     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase=sys.argv[1:]      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      None
										__magic_name__				:							int				=      None
										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							Optional[int]				=      1
										__magic_name__		, __magic_name__				:							int				=      getopt.getopt(_UpperCAmelCase     ,      'i:o:b:s'     ,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
										for opt, arg in opts:
																if opt in ("-i", "--inputdir"):
																						__magic_name__				:							List[str]				=      arg
																elif opt in ("-o", "--outfile"):
																						__magic_name__				:							Union[str, Any]				=      arg
																elif opt in ("-b", "--batch_size"):
																						__magic_name__				:							Any				=      int(_UpperCAmelCase      )
																elif opt in ("-s", "--subset_list"):
																						__magic_name__				:							List[Any]				=      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:
																						__magic_name__				:							str				=      {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase      )}
										else:
																__magic_name__				:							Optional[int]				=      None

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




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      int(file.split('.'      )[0].split('_'      )[-1]      )
										__magic_name__				:							Union[str, Any]				=      os.path.join(self.inputdir     ,      _UpperCAmelCase      )
										return (img_id, filepath)
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							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(_UpperCAmelCase      )      )
																if len(_UpperCAmelCase      ) == self.batch:
																						__magic_name__				:							int				=      batch
																						__magic_name__				:							Union[str, Any]				=      []
																						yield list(map(_UpperCAmelCase     ,      zip(*_UpperCAmelCase      )      )      )

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





				def __call__(  self      ):




										"""simple docstring"""




										if not TEST:
																__magic_name__				:							int				=      datasets.ArrowWriter(features=self.schema     ,      path=self.outputfile      )
										# do file generator
										for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
																__magic_name__		, __magic_name__		, __magic_name__				:							List[Any]				=      self.preprocess(_UpperCAmelCase      )
																__magic_name__				:							Dict				=      self.model(
																    _UpperCAmelCase     ,      _UpperCAmelCase     ,      scales_yx=_UpperCAmelCase     ,      padding='max_detections'     ,      max_detections=self.config.MAX_DETECTIONS     ,      pad_value=0     ,      return_tensors='np'     ,      location='cpu'     ,      )
																__magic_name__				:							Tuple				=      output_dict.pop('normalized_boxes'      )
																if not TEST:
																						__magic_name__				:							Any				=      np.array(_UpperCAmelCase      )
																						__magic_name__				:							str				=      self.schema.encode_batch(_UpperCAmelCase      )
																						writer.write_batch(_UpperCAmelCase      )
																if TEST:
																						break
																# finalizer the writer
										if not TEST:
																__magic_name__		, __magic_name__				:							Optional[Any]				=      writer.finalize()
																print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"      )






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						try:
												__magic_name__				:							List[str]				=      json.load(lowercase__				)
												try:
																		__magic_name__				:							Dict				=      list(data.keys()				)
												except Exception:
																		__magic_name__				:							int				=      [d['img_id'] for d in data]
						except Exception:
												try:
																		__magic_name__				:							str				=      eval(stream.read()				)
												except Exception:
																		__magic_name__				:							Any				=      stream.read().split('\n'				)
						return data


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


