




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()
lowercase__   :    int 					=      logging.get_logger(__name__)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  str:
     a											=   MobileNetVaConfig(layer_norm_eps=0.001)

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

     a											=   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"	,      __UpperCamelCase)
     if matches:
          a											=   float(matches[1])
          a											=   int(matches[2])

     if model_name.startswith("deeplabv3_"):
          a											=   8
          a											=   21
          a											=   "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											=   10_01
          a											=   "imagenet-1k-id2label.json"

     a											=   "huggingface/label-files"
     a											=   json.load(open(hf_hub_download(__UpperCamelCase	,      __UpperCamelCase	,      repo_type="dataset")	,      "r"))

     if config.num_labels == 10_01:
          a											=   {int(__UpperCamelCase) + 1: v for k, v in idalabel.items()}
          a											=   "background"
     else:
          a											=   {int(__UpperCamelCase): v for k, v in idalabel.items()}

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

     return config

def       SCREAMING_SNAKE_CASE							(     )    ->  Dict:
     a											=   "http://images.cocodataset.org/val2017/000000039769.jpg"
     a											=   Image.open(requests.get(__UpperCamelCase	,      stream=__UpperCamelCase).raw)
     return im

@torch.no_grad()
def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase=False)    ->  int:
     a											=   get_mobilenet_va_config(__UpperCamelCase)

     # Load 🤗 model
     if model_name.startswith("deeplabv3_"):
          a											=   MobileNetVaForSemanticSegmentation(__UpperCamelCase).eval()
     else:
          a											=   MobileNetVaForImageClassification(__UpperCamelCase).eval()

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

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

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

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               a											=   torch.tensor(
                   [
                       [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                       [[-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]	,      __UpperCamelCase	,      atol=1e-4)
     else:
          assert logits.shape == (1, 10_01)

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

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

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

     if push_to_hub:
          print("Pushing to the hub...")
          a											=   "google/" + model_name
          image_processor.push_to_hub(__UpperCamelCase)
          model.push_to_hub(__UpperCamelCase)


if __name__ == "__main__":
   lowercase__   :    List[Any] 					=      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."
   )

   lowercase__   :    Union[str, Any] 					=      parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available




@dataclass
class     a__ (      UpperCamelCase__   ):
     a	:    Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
   from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
   from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

