




'''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							:     Optional[int]								=			logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE   (A ) -> str:






       """simple docstring"""







       lowercase__       =				MobileNetVaConfig(layer_norm_eps=0.001 )

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

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

       if model_name.startswith('''deeplabv3_''' ):
              lowercase__       =				8
              lowercase__       =				21
              lowercase__       =				'''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".
              lowercase__       =				1_001
              lowercase__       =				'''imagenet-1k-id2label.json'''

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

       if config.num_labels == 1_001:
              lowercase__       =				{int(_UpperCamelCase ) + 1: v for k, v in idalabel.items()}
              lowercase__       =				'''background'''
       else:
              lowercase__       =				{int(_UpperCamelCase ): v for k, v in idalabel.items()}

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

       return config

def _SCREAMING_SNAKE_CASE   () -> Dict:






       """simple docstring"""







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

@torch.no_grad()
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A=False ) -> int:






       """simple docstring"""







       lowercase__       =				get_mobilenet_va_config(_UpperCamelCase )

       # Load 🤗 model
       if model_name.startswith('''deeplabv3_''' ):
              lowercase__       =				MobileNetVaForSemanticSegmentation(_UpperCamelCase ).eval()
       else:
              lowercase__       =				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
       lowercase__       =				MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}						,				size={'''shortest_edge''': config.image_size + 32}						,				)
       lowercase__       =				image_processor(images=prepare_img()						,				return_tensors='''pt''' )
       lowercase__       =				model(**_UpperCamelCase )
       lowercase__       =				outputs.logits

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

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowercase__       =				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, 1_001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowercase__       =				torch.tensor([0.0_181, -1.0_015, 0.4_688] )
              elif model_name == "mobilenet_v2_1.0_224":
                     lowercase__       =				torch.tensor([0.2_445, -1.1_993, 0.1_905] )
              elif model_name == "mobilenet_v2_0.75_160":
                     lowercase__       =				torch.tensor([0.2_482, 0.4_136, 0.6_669] )
              elif model_name == "mobilenet_v2_0.35_96":
                     lowercase__       =				torch.tensor([0.1_451, -0.4_624, 0.7_192] )
              else:
                     lowercase__       =				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...''' )
              lowercase__       =				'''google/''' + model_name
              image_processor.push_to_hub(_UpperCamelCase )
              model.push_to_hub(_UpperCamelCase )


if __name__ == "__main__":
 lowerCamelCase							:     Dict								=			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							:     Union[str, Any]								=			parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )








'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]



      def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase				)
             lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				do_resize
             lowercase__       =				size
             lowercase__       =				resample
             lowercase__       =				do_center_crop
             lowercase__       =				crop_size
             lowercase__       =				do_rescale
             lowercase__       =				rescale_factor
             lowercase__       =				rescale_offset
             lowercase__       =				do_normalize
             lowercase__       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowercase__       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
             lowercase__       =				include_top



      def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return resize(
                 UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return center_crop(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):

             '''simple docstring'''







             if offset:
                    lowercase__       =				(image - 1_27.5) * scale
                    if data_format is not None:
                           lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                    lowercase__       =				rescaled_image.astype(np.floataa				)
             else:
                    lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
             return rescaled_image



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             return normalize(UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)






      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             lowercase__       =				do_resize if do_resize is not None else self.do_resize
             lowercase__       =				resample if resample is not None else self.resample
             lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
             lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
             lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
             lowercase__       =				image_mean if image_mean is not None else self.image_mean
             lowercase__       =				image_std if image_std is not None else self.image_std
             lowercase__       =				include_top if include_top is not None else self.include_top

             lowercase__       =				size if size is not None else self.size
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else self.crop_size
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				make_list_of_images(UpperCamelCase				)

             if not valid_images(UpperCamelCase				):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

             if do_resize and size is None or resample is None:
                    raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

             if do_center_crop and crop_size is None:
                    raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

             # All transformations expect numpy arrays.
             lowercase__       =				[to_numpy_array(UpperCamelCase				) for image in images]

             if do_resize:
                    lowercase__       =				[self.resize(image=UpperCamelCase     ,		size=UpperCamelCase     ,		resample=UpperCamelCase				) for image in images]

             if do_center_crop:
                    lowercase__       =				[self.center_crop(image=UpperCamelCase     ,		size=UpperCamelCase				) for image in images]

             if do_rescale:
                    lowercase__       =				[self.rescale(image=UpperCamelCase     ,		scale=UpperCamelCase     ,		offset=UpperCamelCase				) for image in images]

             if do_normalize:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase				) for image in images]

             if include_top:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=[0, 0, 0]     ,		std=UpperCamelCase				) for image in images]

             lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]

             lowercase__       =				{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)




