





"""simple docstring"""




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    MobileNetVaConfig(layer_norm_eps=0.001   )

  if "quant" in model_name:
    raise ValueError('Quantized models are not supported.'   )

  A_    :			List[str]									=    re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'					,   snake_case__   )
  if matches:
    A_    :			Optional[int]									=    float(matches[1]   )
    A_    :			Optional[Any]									=    int(matches[2]   )

  if model_name.startswith('deeplabv3_'   ):
    A_    :			str									=    8
    A_    :			List[Any]									=    21
    A_    :			List[Any]									=    'pascal-voc-id2label.json'
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    A_    :			List[Any]									=    1001
    A_    :			int									=    'imagenet-1k-id2label.json'

  A_    :			int									=    'huggingface/label-files'
  A_    :			Union[str, Any]									=    json.load(open(hf_hub_download(snake_case__					,   snake_case__					,   repo_type='dataset'   )					,   'r'   )   )

  if config.num_labels == 1001:
    A_    :			int									=    {int(snake_case__   ) + 1: v for k, v in idalabel.items()}
    A_    :			Optional[int]									=    'background'
  else:
    A_    :			List[Any]									=    {int(snake_case__   ): v for k, v in idalabel.items()}

  A_    :			int									=    idalabel
  A_    :			int									=    {v: k for k, v in idalabel.items()}

  return config





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Dict									=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_    :			Any									=    Image.open(requests.get(snake_case__					,   stream=snake_case__   ).raw   )
  return im





@torch.no_grad()
def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=False   ):






  """simple docstring"""





  A_    :			str									=    get_mobilenet_va_config(snake_case__   )

  # Load 🤗 model
  if model_name.startswith('deeplabv3_'   ):
    A_    :			Union[str, Any]									=    MobileNetVaForSemanticSegmentation(snake_case__   ).eval()
  else:
    A_    :			str									=    MobileNetVaForImageClassification(snake_case__   ).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(snake_case__					,   snake_case__					,   snake_case__   )

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  A_    :			str									=    MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}					,   size={'shortest_edge': config.image_size + 32}					,   )
  A_    :			Tuple									=    image_processor(images=prepare_img()					,   return_tensors='pt'   )
  A_    :			str									=    model(**snake_case__   )
  A_    :			Any									=    outputs.logits

  if model_name.startswith('deeplabv3_'   ):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      A_    :			Optional[int]									=    torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
              [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
          ]   )

    else:
      raise ValueError(f"""Unknown model name: {model_name}"""   )

    assert torch.allclose(logits[0, :3, :3, :3]					,   snake_case__					,   atol=1E-4   )
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      A_    :			List[str]									=    torch.tensor([0.0_181, -1.0_015, 0.4_688]   )
    elif model_name == "mobilenet_v2_1.0_224":
      A_    :			str									=    torch.tensor([0.2_445, -1.1_993, 0.1_905]   )
    elif model_name == "mobilenet_v2_0.75_160":
      A_    :			str									=    torch.tensor([0.2_482, 0.4_136, 0.6_669]   )
    elif model_name == "mobilenet_v2_0.35_96":
      A_    :			Any									=    torch.tensor([0.1_451, -0.4_624, 0.7_192]   )
    else:
      A_    :			Dict									=    None

    if expected_logits is not None:
      assert torch.allclose(logits[0, :3]					,   snake_case__					,   atol=1E-4   )

  Path(snake_case__   ).mkdir(exist_ok=snake_case__   )
  print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
  model.save_pretrained(snake_case__   )
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
  image_processor.save_pretrained(snake_case__   )

  if push_to_hub:
    print('Pushing to the hub...'   )
    A_    :			Optional[int]									=    'google/' + model_name
    image_processor.push_to_hub(snake_case__   )
    model.push_to_hub(snake_case__   )


if __name__ == "__main__":
   lowerCamelCase_	:		Optional[int]         =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='mobilenet_v2_1.0_224',
       type=str,
       help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
   )
   parser.add_argument(
       '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   lowerCamelCase_	:		List[str]         =   parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )

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
__magic_name__  :  Dict     			=   False
__magic_name__  :  Union[str, Any]     			=   Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__magic_name__  :  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_0_4_8), 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						,    __UpperCamelCase=sys.argv[1:]					):
      A_								=					None
      A_								=					None
      A_								=					None
      A_								=					1
      A_	,	A_								=					getopt.getopt(__UpperCamelCase						,    "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(__UpperCamelCase					)
          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(__UpperCamelCase					), f'{outputfile}'
      if subset_list is not None:
          with open(os.path.realpath(__UpperCamelCase					)					) as f:
              A_								=					{self._vqa_file_split()[0] for x in tryload(__UpperCamelCase					)}
      else:
          A_								=					None

      A_								=					CONFIG
      if torch.cuda.is_available():
          A_								=					"cuda"
      A_								=					os.path.realpath(__UpperCamelCase					)
      A_								=					os.path.realpath(__UpperCamelCase					)
      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 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					int(file.split("."					)[0].split("_"					)[-1]					)
      A_								=					os.path.join(self.inputdir						,    __UpperCamelCase					)
      return (img_id, filepath)

  @property
  def 					lowercase_						(					self					):
      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(__UpperCamelCase					)					)
          if len(__UpperCamelCase					) == self.batch:
              A_								=					batch
              A_								=					[]
              yield list(map(__UpperCamelCase						,    zip(*__UpperCamelCase					)					)					)

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

  def __call__(					self					):
      # make writer
      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_	,	A_	,	A_								=					self.preprocess(__UpperCamelCase					)
          A_								=					self.model(
              __UpperCamelCase						,    __UpperCamelCase						,    scales_yx=__UpperCamelCase						,    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(__UpperCamelCase					)
              A_								=					self.schema.encode_batch(__UpperCamelCase					)
              writer.write_batch(__UpperCamelCase					)
          if TEST:
              break
          # finalizer the writer
      if not TEST:
          A_	,	A_								=					writer.finalize()
          print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'					)





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    try:
        A_								=					json.load(snake_case__ )
        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__":
     __magic_name__  :  List[str]     			=   Extract(sys.argv[1:])
     extract()
     if not TEST:
          __magic_name__  :  List[Any]     			=   datasets.Dataset.from_file(extract.outputfile)
          # wala!
          # print(np.array(dataset[0:2]["roi_features"]).shape)






