

'''simple docstring'''

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate





class 							_snake_case	(    _a  ):
   _A    :       Dict      				=   CONFIG_NAME
   @register_to_config
   def __init__(	self     : str       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : int = 8       ,SCREAMING_SNAKE_CASE__     : str = "lanczos"       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : bool = False       ,):
     super().__init__()
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : np.ndarray							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Optional[int]   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Optional[Any]   =   (images * 255).round().astype("uint8"							)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       SCREAMING_SNAKE_CASE:Dict   =   [Image.fromarray(image.squeeze()       ,mode="L"							) for image in images]
     else:
       SCREAMING_SNAKE_CASE:Any   =   [Image.fromarray(SCREAMING_SNAKE_CASE__							) for image in images]

     return pil_images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Union[List[PIL.Image.Image], PIL.Image.Image]							):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any   =   [images]
     SCREAMING_SNAKE_CASE:str   =   [np.array(SCREAMING_SNAKE_CASE__							).astype(np.floataa							) / 255.0 for image in images]
     SCREAMING_SNAKE_CASE:List[Any]   =   np.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)

     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : np.ndarray							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Optional[int]   =   images[..., None]

     SCREAMING_SNAKE_CASE:Any   =   torch.from_numpy(images.transpose(0       ,3       ,1       ,2							)							)
     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : torch.FloatTensor							):
     SCREAMING_SNAKE_CASE:int   =   images.cpu().permute(0       ,2       ,3       ,1							).float().numpy()
     return images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : List[str]							):
     return 2.0 * images - 1.0
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Dict							):
     return (images / 2 + 0.5).clamp(0       ,1							)
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : PIL.Image.Image							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   image.convert("RGB"							)
     return image
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : PIL.Image.Image       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,):
     if height is None:
       SCREAMING_SNAKE_CASE:Any   =   image.height
     if width is None:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   image.width

     SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Union[str, Any]   =   (
         x - x % self.config.vae_scale_factor for x in (width, height)
     )  # resize to integer multiple of vae_scale_factor
     SCREAMING_SNAKE_CASE:str   =   image.resize((width, height)       ,resample=PIL_INTERPOLATION[self.config.resample]							)
     return image
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,SCREAMING_SNAKE_CASE__     : Optional[int] = None       ,):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   (PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any   =   [image]
     elif not (isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) and all(isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for i in image							)):
       raise ValueError(
           F'''Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE__							) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE__							)}'''							)

     if isinstance(image[0]       ,PIL.Image.Image							):
       if self.config.do_convert_rgb:
         SCREAMING_SNAKE_CASE:str   =   [self.convert_to_rgb(SCREAMING_SNAKE_CASE__							) for i in image]
       if self.config.do_resize:
         SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.resize(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for i in image]
       SCREAMING_SNAKE_CASE:List[Any]   =   self.pil_to_numpy(SCREAMING_SNAKE_CASE__							)  # to np
       SCREAMING_SNAKE_CASE:Optional[Any]   =   self.numpy_to_pt(SCREAMING_SNAKE_CASE__							)  # to pt

     elif isinstance(image[0]       ,np.ndarray							):
       SCREAMING_SNAKE_CASE:Any   =   np.concatenate(SCREAMING_SNAKE_CASE__       ,axis=0							) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)
       SCREAMING_SNAKE_CASE:Optional[Any]   =   self.numpy_to_pt(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Optional[Any]   =   image.shape
       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
         raise ValueError(
             F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

     elif isinstance(image[0]       ,torch.Tensor							):
       SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.cat(SCREAMING_SNAKE_CASE__       ,axis=0							) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__       ,axis=0							)
       SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:str   =   image.shape

       # don't need any preprocess if the image is latents
       if channel == 4:
         return image

       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
         raise ValueError(
             F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

        # expected range [0,1], normalize to [-1,1]
     SCREAMING_SNAKE_CASE:int   =   self.config.do_normalize
     if image.min() < 0:
       warnings.warn(
           "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
           F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''       ,SCREAMING_SNAKE_CASE__       ,)
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   False

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[str]   =   self.normalize(SCREAMING_SNAKE_CASE__							)

     return image







   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : torch.FloatTensor       ,SCREAMING_SNAKE_CASE__     : str = "pil"       ,SCREAMING_SNAKE_CASE__     : Optional[List[bool]] = None       ,):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor							):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__							)}. We only support pytorch tensor'''							)
     if output_type not in ["latent", "pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE:List[Any]   =   (
           F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type"       ,"1.0.0"       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[str]   =   "np"

     if output_type == "latent":
       return image

     if do_denormalize is None:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.config.do_normalize] * image.shape[0]

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   torch.stack(
         [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

     if output_type == "pt":
       return image

     SCREAMING_SNAKE_CASE:Tuple   =   self.pt_to_numpy(SCREAMING_SNAKE_CASE__							)

     if output_type == "np":
       return image

     if output_type == "pil":
       return self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)





class 							_snake_case	(    _a  ):
   _A    :       Any      				=   CONFIG_NAME
   @register_to_config
   def __init__(	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : int = 8       ,SCREAMING_SNAKE_CASE__     : str = "lanczos"       ,SCREAMING_SNAKE_CASE__     : bool = True       ,):
     super().__init__()
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : Optional[int]							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Tuple   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   (images * 255).round().astype("uint8"							)
     if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       SCREAMING_SNAKE_CASE:str   =   [Image.fromarray(image.squeeze()       ,mode="L"							) for image in images]
     else:
       SCREAMING_SNAKE_CASE:Tuple   =   [Image.fromarray(image[:, :, :3]							) for image in images]

     return pil_images
   @staticmethod
   def       __UpperCamelCase  (	SCREAMING_SNAKE_CASE__     : int							):
     return image[:, :, 1] * 2**8 + image[:, :, 2]
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : Optional[Any]							):
     if images.ndim == 3:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   images[None, ...]
     SCREAMING_SNAKE_CASE:Optional[Any]   =   images[:, :, :, 3:]
     if images.shape[-1] == 6:
       SCREAMING_SNAKE_CASE:Optional[int]   =   (images_depth * 255).round().astype("uint8"							)
       SCREAMING_SNAKE_CASE:Tuple   =   [
           Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE__							)       ,mode="I;16"							) for image_depth in images_depth
       ]
     elif images.shape[-1] == 4:
       SCREAMING_SNAKE_CASE:List[str]   =   (images_depth * 65_535.0).astype(np.uintaa							)
       SCREAMING_SNAKE_CASE:Dict   =   [Image.fromarray(SCREAMING_SNAKE_CASE__       ,mode="I;16"							) for image_depth in images_depth]
     else:
       raise Exception("Not supported"							)

     return pil_images







   def       __UpperCamelCase  (	self     : Any       ,SCREAMING_SNAKE_CASE__     : torch.FloatTensor       ,SCREAMING_SNAKE_CASE__     : str = "pil"       ,SCREAMING_SNAKE_CASE__     : Optional[List[bool]] = None       ,):
     if not isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor							):
       raise ValueError(
           F'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE__							)}. We only support pytorch tensor'''							)
     if output_type not in ["latent", "pt", "np", "pil"]:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   (
           F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type"       ,"1.0.0"       ,SCREAMING_SNAKE_CASE__       ,standard_warn=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[str]   =   "np"

     if do_denormalize is None:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.config.do_normalize] * image.shape[0]

     SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.stack(
         [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.pt_to_numpy(SCREAMING_SNAKE_CASE__							)

     if output_type == "np":
       if image.shape[-1] == 6:
         SCREAMING_SNAKE_CASE:int   =   np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]       ,axis=0							)
       else:
         SCREAMING_SNAKE_CASE:Tuple   =   image[:, :, :, 3:]
       return image[:, :, :, :3], image_depth

     if output_type == "pil":
       return self.numpy_to_pil(SCREAMING_SNAKE_CASE__							), self.numpy_to_depth(SCREAMING_SNAKE_CASE__							)
     else:
       raise Exception(F'''This type {output_type} is not supported'''							)









'''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()
A_      =							logging.get_logger(__name__)





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   MobileNetVaConfig(layer_norm_eps=0.001	)

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

  SCREAMING_SNAKE_CASE:Any   =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$" ,    snake_case	)
  if matches:
    SCREAMING_SNAKE_CASE:str   =   float(matches[1]	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   int(matches[2]	)

  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:str   =   8
    SCREAMING_SNAKE_CASE:Optional[int]   =   21
    SCREAMING_SNAKE_CASE:Dict   =   "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".
    SCREAMING_SNAKE_CASE:Optional[int]   =   1001
    SCREAMING_SNAKE_CASE:Tuple   =   "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]   =   "huggingface/label-files"
  SCREAMING_SNAKE_CASE:List[Any]   =   json.load(open(hf_hub_download(snake_case ,    snake_case ,    repo_type="dataset"	) ,    "r"	)	)

  if config.num_labels == 1001:
    SCREAMING_SNAKE_CASE:Optional[Any]   =   {int(snake_case	) + 1: v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Dict   =   "background"
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   {int(snake_case	): v for k, v in idalabel.items()}

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

  return config





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str   =   Image.open(requests.get(snake_case ,    stream=snake_case	).raw	)
  return im





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case=False	):
  SCREAMING_SNAKE_CASE:Any   =   get_mobilenet_va_config(snake_case	)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:Tuple   =   MobileNetVaForSemanticSegmentation(snake_case	).eval()
  else:
    SCREAMING_SNAKE_CASE:List[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
  SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size} ,    size={"shortest_edge": config.image_size + 32} ,    )
  SCREAMING_SNAKE_CASE:List[Any]   =   image_processor(images=prepare_img() ,    return_tensors="pt"	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   model(**snake_case	)
  SCREAMING_SNAKE_CASE:str   =   outputs.logits

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

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]	)

    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":
      SCREAMING_SNAKE_CASE:List[str]   =   torch.tensor([0.0181, -1.0015, 0.4688]	)
    elif model_name == "mobilenet_v2_1.0_224":
      SCREAMING_SNAKE_CASE:Optional[int]   =   torch.tensor([0.2445, -1.1993, 0.1905]	)
    elif model_name == "mobilenet_v2_0.75_160":
      SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.tensor([0.2482, 0.4136, 0.6669]	)
    elif model_name == "mobilenet_v2_0.35_96":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor([0.1451, -0.4624, 0.7192]	)
    else:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   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..."	)
    SCREAMING_SNAKE_CASE:Tuple   =   "google/" + model_name
    image_processor.push_to_hub(snake_case	)
    model.push_to_hub(snake_case	)


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

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






