


def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



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()
UpperCAmelCase			=    logging.get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Any:




      """simple docstring"""

      snake_case_       =      MobileNetVaConfig(layer_norm_eps=0.0_0_1					)

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

      snake_case_       =      re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''					,					SCREAMING_SNAKE_CASE					)
      if matches:
            snake_case_       =      float(matches[1]					)
            snake_case_       =      int(matches[2]					)

      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      8
            snake_case_       =      21
            snake_case_       =      '''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".
            snake_case_       =      1001
            snake_case_       =      '''imagenet-1k-id2label.json'''

      snake_case_       =      '''huggingface/label-files'''
      snake_case_       =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					repo_type='''dataset'''					)					,					'''r'''					)					)

      if config.num_labels == 1001:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					) + 1: v for k, v in idalabel.items()}
            snake_case_       =      '''background'''
      else:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}

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

      return config






def        __lowerCAmelCase    ()->  str:




      """simple docstring"""

      snake_case_       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_       =      Image.open(requests.get(SCREAMING_SNAKE_CASE					,					stream=SCREAMING_SNAKE_CASE					).raw					)
      return im






@torch.no_grad()
def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=False					)->  str:




      """simple docstring"""

      snake_case_       =      get_mobilenet_va_config(SCREAMING_SNAKE_CASE					)

      # Load 🤗 model
      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE					).eval()
      else:
            snake_case_       =      MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE					).eval()

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

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

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

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  snake_case_       =      torch.tensor(
                      [
                          [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                          [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                          [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                      ]					)

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

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

            if model_name == "mobilenet_v2_1.4_224":
                  snake_case_       =      torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]					)
            elif model_name == "mobilenet_v2_1.0_224":
                  snake_case_       =      torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]					)
            elif model_name == "mobilenet_v2_0.75_160":
                  snake_case_       =      torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]					)
            elif model_name == "mobilenet_v2_0.35_96":
                  snake_case_       =      torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]					)
            else:
                  snake_case_       =      None

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

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

      if push_to_hub:
            print('''Pushing to the hub...'''					)
            snake_case_       =      '''google/''' + model_name
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE					)
            model.push_to_hub(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   UpperCAmelCase			=    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."""
   )

   UpperCAmelCase			=    parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )