

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, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)







class   __lowercase				:
 def __init__(							self				,							A_=sys.argv[1:] ) ->List[Any]:



       '''simple docstring'''





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

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



       '''simple docstring'''





       __lowerCAmelCase :						Any = int(file.split('''.''' )[0].split('''_''' )[-1] )
       __lowerCAmelCase :						Optional[Any] = os.path.join(self.inputdir				,							A_ )
       return (img_id, filepath)
 @property
 def      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = []
       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 :						Optional[Any] = batch
                   __lowerCAmelCase :						List[Any] = []
                   yield list(map(A_				,							zip(*A_ ) ) )

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





 def __call__(							self ) ->Dict:



       '''simple docstring'''





       if not TEST:
             __lowerCAmelCase :						List[str] = datasets.ArrowWriter(features=self.schema				,							path=self.outputfile )
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator ):
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = self.preprocess(A_ )
             __lowerCAmelCase :						int = 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 :						Dict = output_dict.pop('''normalized_boxes''' )
             if not TEST:
                   __lowerCAmelCase :						Any = np.array(A_ )
                   __lowerCAmelCase :						Optional[Any] = self.schema.encode_batch(A_ )
                   writer.write_batch(A_ )
             if TEST:
                   break
             # finalizer the writer
       if not TEST:
             __lowerCAmelCase,							__lowerCAmelCase :						Any = writer.finalize()
             print(f"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb""" )







def 		_lowercase			(					lowercase__						):
      try:
            __lowerCAmelCase :						Any = json.load(lowercase__						)
            try:
                  __lowerCAmelCase :						str = list(data.keys()						)
            except Exception:
                  __lowerCAmelCase :						List[str] = [d['''img_id'''] for d in data]
      except Exception:
            try:
                  __lowerCAmelCase :						Tuple = eval(stream.read()						)
            except Exception:
                  __lowerCAmelCase :						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)





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 		_lowercase			(					):
      __lowerCAmelCase :						Union[str, 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"]:
            __lowerCAmelCase :						Any = getattr(transformers.models.auto       ,     lowercase__       ,     lowercase__						)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __lowerCAmelCase :						Optional[int] = [
                x
                for x in dir(lowercase__						)
                if x.endswith('''_MAPPING_NAMES'''						)
                and (x.startswith('''MODEL_'''						) or x.startswith('''TF_MODEL_'''						) or x.startswith('''FLAX_MODEL_'''						))
            ]
            for name in mapping_names:
                  __lowerCAmelCase :						int = getattr(lowercase__       ,     lowercase__						)
                  if mapping is not None:
                        for v in mapping.values():
                              if isinstance(lowercase__       ,     (list, tuple)						):
                                    model_names.update(lowercase__						)
                              elif isinstance(lowercase__       ,     lowercase__						):
                                    model_names.add(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					):
      # All model names defined in auto mappings
      __lowerCAmelCase :						int = set(get_all_model_names()						)

      with open('''tests/utils/tiny_model_summary.json'''						) as fp:
            __lowerCAmelCase :						Tuple = json.load(lowercase__						)
      __lowerCAmelCase :						Tuple = 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.
      __lowerCAmelCase :						List[Any] = model_names.difference(lowercase__						)
      for model_name in copy.copy(lowercase__						):
            if not model_name.startswith('''TF'''						) and f"""TF{model_name}""" in not_on_hub:
                  tiny_models_names.remove(lowercase__						)
            elif model_name.startswith('''TF'''						) and model_name[2:] in not_on_hub:
                  tiny_models_names.remove(lowercase__						)

      return sorted(lowercase__						)







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      __lowerCAmelCase :						Optional[Any] = get_all_model_names()
      __lowerCAmelCase :						str = hf_api.list_models(
          filter=ModelFilter(
              author='''hf-internal-testing'''       ,     )						)
      __lowerCAmelCase :						List[str] = set()
      for x in models:
            __lowerCAmelCase :						str = x.modelId
            __lowerCAmelCase,							__lowerCAmelCase :						List[Any] = model.split('''/'''						)
            if not model.startswith('''tiny-random-'''						):
                  continue
            __lowerCAmelCase :						Dict = 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(lowercase__						)

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

            __lowerCAmelCase :						Tuple = sorted(content['''tokenizer_classes''']						)
            __lowerCAmelCase :						List[Any] = sorted(content['''processor_classes''']						)
            __lowerCAmelCase :						int = sorted(content['''model_classes''']						)

            __lowerCAmelCase :						Union[str, Any] = content
            with open(os.path.join(lowercase__       ,     '''hub_tiny_model_summary.json'''						)       ,     '''w'''						) as fp:
                  json.dump(lowercase__       ,     lowercase__       ,     ensure_ascii=lowercase__       ,     indent=4						)


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

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

 _UpperCamelCase			      =		"tiny_models"
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		None
 _UpperCamelCase			      =		get_tiny_model_names_from_repo()
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		True
 _UpperCamelCase			      =		"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,
 )


