




'''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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

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

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

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

     if config.num_labels == 1001:
          a_    :       Union[str, Any]    	=     {int(lowercase_							) + 1: v for k, v in idalabel.items()}
          a_    :       Tuple    	=     'background'
     else:
          a_    :       Tuple    	=     {int(lowercase_							): v for k, v in idalabel.items()}

     a_    :       List[Any]    	=     idalabel
     a_    :       List[Any]    	=     {v: k for k, v in idalabel.items()}

     return config



def    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


     a_    :       List[Any]    	=     'http://images.cocodataset.org/val2017/000000039769.jpg'
     a_    :       Optional[Any]    	=     Image.open(requests.get(lowercase_   ,						stream=lowercase_							).raw							)
     return im



@torch.no_grad()
def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int   ,						UpperCamelCase__						:				Any   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Union[str, Any]=False							):




     """simple docstring"""


     a_    :       Optional[Any]    	=     get_mobilenet_va_config(lowercase_							)

     # Load 🤗 model
     if model_name.startswith("""deeplabv3_"""							):
          a_    :       Any    	=     MobileNetVaForSemanticSegmentation(lowercase_							).eval()
     else:
          a_    :       str    	=     MobileNetVaForImageClassification(lowercase_							).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     a_    :       Optional[int]    	=     MobileNetVaImageProcessor(
         crop_size={"""width""": config.image_size, """height""": config.image_size}   ,						size={"""shortest_edge""": config.image_size + 32}   ,						)
     a_    :       int    	=     image_processor(images=prepare_img()   ,						return_tensors="""pt"""							)
     a_    :       List[Any]    	=     model(**lowercase_							)
     a_    :       str    	=     outputs.logits

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

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               a_    :       str    	=     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]   ,						lowercase_   ,						atol=1E-4							)
     else:
          assert logits.shape == (1, 1001)

          if model_name == "mobilenet_v2_1.4_224":
               a_    :       Dict    	=     torch.tensor([0.0181, -1.0015, 0.4688]							)
          elif model_name == "mobilenet_v2_1.0_224":
               a_    :       str    	=     torch.tensor([0.2445, -1.1993, 0.1905]							)
          elif model_name == "mobilenet_v2_0.75_160":
               a_    :       List[str]    	=     torch.tensor([0.2482, 0.4136, 0.6669]							)
          elif model_name == "mobilenet_v2_0.35_96":
               a_    :       Optional[int]    	=     torch.tensor([0.1451, -0.4624, 0.7192]							)
          else:
               a_    :       int    	=     None

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

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

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


if __name__ == "__main__":
 lowerCAmelCase_			:     Union[str, 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.'
 )

 lowerCAmelCase_			:     str							    =     parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






'''simple docstring'''


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCAmelCase_			:     Tuple							    =     1000







class     SCREAMING_SNAKE_CASE						:






      def __init__(   self      :     Union[str, Any]	,	lowercase__      :     Optional[int]	,	lowercase__      :     Optional[int]=1.2	,	lowercase__      :     Tuple="g"	,	lowercase__      :     str=0.5	,	lowercase__      :     List[str]="-"	,	lowercase__      :     Union[str, Any]="test_out.jpg"	,	lowercase__      :     Dict=True	,	lowercase__      :     Optional[int]=False	,	lowercase__      :     str=None	,	lowercase__      :     Union[str, Any]=None	,	lowercase__      :     Tuple=0.7	,	):


           '''simple docstring'''

           if isinstance(lowercase__	,	torch.Tensor					):
                a_    :       Union[str, Any]    	=     img.numpy().astype("""np.uint8"""					)
           if isinstance(lowercase__	,	lowercase__					):
                a_    :       List[Any]    	=     img_tensorize(lowercase__					)
           assert isinstance(lowercase__	,	np.ndarray					)

           a_	,    a_    :       int    	=     img.shape[1], img.shape[0]
           a_    :       str    	=     mplfigure.Figure(frameon=lowercase__					)
           a_    :       Optional[int]    	=     fig.get_dpi()
           a_    :       List[Any]    	=     (width * scale + 1e-2) / dpi
           a_    :       Union[str, Any]    	=     (height * scale + 1e-2) / dpi
           fig.set_size_inches(lowercase__	,	lowercase__					)
           a_    :       Optional[Any]    	=     fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
           ax.axis("""off"""					)
           ax.set_xlim(0.0	,	lowercase__					)
           ax.set_ylim(lowercase__					)

           a_    :       Union[str, Any]    	=     saveas
           a_    :       List[Any]    	=     rgb
           a_    :       Optional[int]    	=     pynb
           a_    :       Any    	=     img
           a_    :       List[Any]    	=     edgecolor
           a_    :       str    	=     0.5
           a_    :       Optional[int]    	=     linestyle
           a_    :       int    	=     int(np.sqrt(min(lowercase__	,	lowercase__					)					) * scale // 3					)
           a_    :       Optional[int]    	=     width
           a_    :       Union[str, Any]    	=     height
           a_    :       List[Any]    	=     scale
           a_    :       Any    	=     fig
           a_    :       List[Any]    	=     ax
           a_    :       Union[str, Any]    	=     pad
           a_    :       List[str]    	=     idaobj
           a_    :       List[Any]    	=     idaattr
           a_    :       Union[str, Any]    	=     FigureCanvasAgg(lowercase__					)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     str=None					):


           '''simple docstring'''

           if color is None:
                a_    :       int    	=     self.edgecolor
           ((a_)	,    (a_)	,    (a_)	,    (a_))    :       Union[str, Any]    	=     box
           a_    :       Union[str, Any]    	=     xa - xa
           a_    :       List[Any]    	=     ya - ya
           self.ax.add_patch(
               mpl.patches.Rectangle(
                   (xa, ya)	,	lowercase__	,	lowercase__	,	fill=lowercase__	,	edgecolor=lowercase__	,	linewidth=self.font_size // 3	,	alpha=self.alpha	,	linestyle=self.linestyle	,	)					)






      def        lowercase_	(   self      :     int	,	lowercase__      :     List[Any]	,	lowercase__      :     List[str]=None	,	lowercase__      :     Optional[Any]=None	,	lowercase__      :     List[Any]=None	,	lowercase__      :     Union[str, Any]=None					):


           '''simple docstring'''

           if len(boxes.shape					) > 2:
                a_    :       List[Any]    	=     boxes[0]
           if len(obj_ids.shape					) > 1:
                a_    :       List[Any]    	=     obj_ids[0]
           if len(obj_scores.shape					) > 1:
                a_    :       Optional[int]    	=     obj_scores[0]
           if len(attr_ids.shape					) > 1:
                a_    :       int    	=     attr_ids[0]
           if len(attr_scores.shape					) > 1:
                a_    :       Dict    	=     attr_scores[0]
           if isinstance(lowercase__	,	torch.Tensor					):
                a_    :       Optional[Any]    	=     boxes.numpy()
           if isinstance(lowercase__	,	lowercase__					):
                a_    :       List[Any]    	=     np.array(lowercase__					)
           assert isinstance(lowercase__	,	np.ndarray					)
           a_    :       Optional[int]    	=     np.prod(boxes[:, 2:] - boxes[:, :2]	,	axis=1					)
           a_    :       List[str]    	=     np.argsort(-areas					).tolist()
           a_    :       List[Any]    	=     boxes[sorted_idxs] if boxes is not None else None
           a_    :       List[Any]    	=     obj_ids[sorted_idxs] if obj_ids is not None else None
           a_    :       Any    	=     obj_scores[sorted_idxs] if obj_scores is not None else None
           a_    :       List[Any]    	=     attr_ids[sorted_idxs] if attr_ids is not None else None
           a_    :       str    	=     attr_scores[sorted_idxs] if attr_scores is not None else None

           a_    :       Tuple    	=     [self._random_color(maximum=1					) for _ in range(len(lowercase__					)					)]
           a_    :       List[Any]    	=     [assigned_colors[idx] for idx in sorted_idxs]
           if obj_ids is not None:
                a_    :       Dict    	=     self._create_text_labels_attr(lowercase__	,	lowercase__	,	lowercase__	,	lowercase__					)
                for i in range(len(lowercase__					)					):
                     a_    :       Optional[int]    	=     assigned_colors[i]
                     self.add_box(boxes[i]	,	lowercase__					)
                     self.draw_labels(labels[i]	,	boxes[i]	,	lowercase__					)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Any	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           a_	,    a_	,    a_	,    a_    :       int    	=     box
           a_    :       List[str]    	=     (xa, ya)
           a_    :       Optional[int]    	=     (ya - ya) * (xa - xa)
           a_    :       Optional[Any]    	=     _SMALL_OBJ * self.scale
           if instance_area < small or ya - ya < 40 * self.scale:
                if ya >= self.height - 5:
                     a_    :       Dict    	=     (xa, ya)
                else:
                     a_    :       str    	=     (xa, ya)

           a_    :       List[Any]    	=     (ya - ya) / np.sqrt(self.height * self.width					)
           a_    :       Optional[int]    	=     self._change_color_brightness(lowercase__	,	brightness_factor=0.7					)
           a_    :       List[str]    	=     np.clip((height_ratio - 0.02) / 0.08 + 1	,	1.2	,	2					)
           font_size *= 0.75 * self.font_size

           self.draw_text(
               text=lowercase__	,	position=lowercase__	,	color=lowercase__	,	)






      def        lowercase_	(   self      :     Any	,	lowercase__      :     Optional[int]	,	lowercase__      :     Tuple	,	lowercase__      :     str="g"	,	lowercase__      :     Optional[Any]="left"	,	):


           '''simple docstring'''

           a_    :       List[Any]    	=     0
           a_    :       Tuple    	=     self.font_size
           a_    :       Tuple    	=     np.maximum(list(mplc.to_rgb(lowercase__					)					)	,	0.2					)
           a_    :       List[str]    	=     max(0.8	,	np.max(lowercase__					)					)
           a_    :       str    	=     {
               """facecolor""": """black""",
               """alpha""": self.alpha,
               """pad""": self.pad,
               """edgecolor""": """none""",
           }
           a_	,    a_    :       Any    	=     position
           self.ax.text(
               lowercase__	,	lowercase__	,	lowercase__	,	size=font_size * self.scale	,	family="""sans-serif"""	,	bbox=lowercase__	,	verticalalignment="""top"""	,	horizontalalignment=lowercase__	,	color=lowercase__	,	zorder=10	,	rotation=lowercase__	,	)






      def        lowercase_	(   self      :     List[Any]	,	lowercase__      :     Optional[Any]=None					):


           '''simple docstring'''

           if saveas is None:
                a_    :       Dict    	=     self.saveas
           if saveas.lower().endswith(""".jpg"""					) or saveas.lower().endswith(""".png"""					):
                cva.imwrite(
                    lowercase__	,	self._get_buffer()[:, :, ::-1]	,	)
           else:
                self.fig.savefig(lowercase__					)






      def        lowercase_	(   self      :     List[Any]	,	lowercase__      :     int	,	lowercase__      :     Tuple	,	lowercase__      :     Optional[int]	,	lowercase__      :     Tuple					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     [self.idaobj[i] for i in classes]
           a_    :       Any    	=     [self.idaattr[i] for i in attr_classes]
           a_    :       List[str]    	=     [
               F"{label} {score:.2f} {attr} {attr_score:.2f}"
               for label, score, attr, attr_score in zip(lowercase__	,	lowercase__	,	lowercase__	,	lowercase__					)
           ]
           return labels






      def        lowercase_	(   self      :     str	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     [self.idaobj[i] for i in classes]
           if scores is not None:
                if labels is None:
                     a_    :       int    	=     ["""{:.0f}%""".format(s * 100					) for s in scores]
                else:
                     a_    :       Optional[int]    	=     ["""{} {:.0f}%""".format(lowercase__	,	s * 100					) for li, s in zip(lowercase__	,	lowercase__					)]
           return labels






      def        lowercase_	(   self      :     Dict	,	lowercase__      :     Union[str, Any]=255					):


           '''simple docstring'''

           a_    :       int    	=     np.random.randint(0	,	len(_COLORS					)					)
           a_    :       Tuple    	=     _COLORS[idx] * maximum
           if not self.rgb:
                a_    :       str    	=     ret[::-1]
           return ret






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           if not self.pynb:
                a_	,    a_    :       int    	=     self.canvas.print_to_buffer()
                if (width, height) != (self.width, self.height):
                     a_    :       Union[str, Any]    	=     cva.resize(self.img	,	(width, height)					)
                else:
                     a_    :       int    	=     self.img
           else:
                a_    :       Optional[Any]    	=     io.BytesIO()  # works for cairo backend
                self.canvas.print_rgba(lowercase__					)
                a_	,    a_    :       List[Any]    	=     self.width, self.height
                a_    :       Tuple    	=     buf.getvalue()
                a_    :       Tuple    	=     self.img

           a_    :       Optional[Any]    	=     np.frombuffer(lowercase__	,	dtype="""uint8"""					)
           a_    :       Union[str, Any]    	=     buffer.reshape(lowercase__	,	lowercase__	,	4					)
           a_	,    a_    :       Optional[Any]    	=     np.split(lowercase__	,	[3]	,	axis=2					)

           try:
                import numexpr as ne  # fuse them with numexpr

                a_    :       Dict    	=     ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""					)
           except ImportError:
                a_    :       List[Any]    	=     alpha.astype("""float32"""					) / 255.0
                a_    :       Optional[int]    	=     img * (1 - alpha) + rgb * alpha

           return visualized_image.astype("""uint8"""					)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Any					):


           '''simple docstring'''

           assert brightness_factor >= -1.0 and brightness_factor <= 1.0
           a_    :       str    	=     mplc.to_rgb(lowercase__					)
           a_    :       Optional[int]    	=     colorsys.rgb_to_hls(*mplc.to_rgb(lowercase__					)					)
           a_    :       Union[str, Any]    	=     polygon_color[1] + (brightness_factor * polygon_color[1])
           a_    :       int    	=     0.0 if modified_lightness < 0.0 else modified_lightness
           a_    :       Any    	=     1.0 if modified_lightness > 1.0 else modified_lightness
           a_    :       Optional[int]    	=     colorsys.hls_to_rgb(polygon_color[0]	,	lowercase__	,	polygon_color[2]					)
           return modified_color

lowerCAmelCase_			:     List[Any]							    =     (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
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        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)
