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 SCREAMING_SNAKE_CASE				(     lowercase_:	Dict				):
       _UpperCamelCase     :       Union[str, Any]  						=  MobileNetVaConfig(layer_norm_eps=0.001				)

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

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

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

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

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

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

       return config


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


@torch.no_grad()
def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	Any		,						lowercase_:	Optional[Any]		,						lowercase_:	Union[str, Any]=False				):
       _UpperCamelCase     :       Optional[Any]  						=  get_mobilenet_va_config(lowercase_				)

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

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

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     _UpperCamelCase     :       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":
                     _UpperCamelCase     :       Dict  						=  torch.tensor([0.0181, -1.0015, 0.4688]				)
              elif model_name == "mobilenet_v2_1.0_224":
                     _UpperCamelCase     :       str  						=  torch.tensor([0.2445, -1.1993, 0.1905]				)
              elif model_name == "mobilenet_v2_0.75_160":
                     _UpperCamelCase     :       List[str]  						=  torch.tensor([0.2482, 0.4136, 0.6669]				)
              elif model_name == "mobilenet_v2_0.35_96":
                     _UpperCamelCase     :       Optional[int]  						=  torch.tensor([0.1451, -0.4624, 0.7192]				)
              else:
                     _UpperCamelCase     :       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...'				)
              _UpperCamelCase     :       Optional[int]  						=  'google/' + model_name
              image_processor.push_to_hub(lowercase_				)
              model.push_to_hub(lowercase_				)


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
 )




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]=10		,						lowercase_:	Optional[Any]=10		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Any=False				):
       _UpperCamelCase     :       List[Any]  						=  []
       for i in range(image_processor_tester.num_frames				):
              video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta				)				)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase     :       Tuple  						=  [Image.fromarray(np.moveaxis(lowercase_		,						0		,						-1				)				) for frame in video]

       if torchify:
              _UpperCamelCase     :       Union[str, Any]  						=  [torch.from_numpy(lowercase_				) for frame in video]

       return video


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Tuple=False		,						lowercase_:	Union[str, Any]=False				):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase     :       Any  						=  []
       for i in range(image_processor_tester.batch_size				):
              if equal_resolution:
                     _UpperCamelCase     :       str  						=  image_processor_tester.max_resolution
              else:
                     _UpperCamelCase						,      _UpperCamelCase     :       int  						=  np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution				)		,						2				)
                     _UpperCamelCase     :       Optional[Any]  						=  prepare_video(
                         image_processor_tester=lowercase_		,						width=lowercase_		,						height=lowercase_		,						numpify=lowercase_		,						torchify=lowercase_		,						)
              video_inputs.append(lowercase_				)

       return video_inputs




class    __lowerCAmelCase  (       unittest.TestCase						):



 '''simple docstring'''

 def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]=7       ,			lowerCamelCase       :		List[Any]=3       ,			lowerCamelCase       :		str=4       ,			lowerCamelCase       :		Union[str, Any]=1_8       ,			lowerCamelCase       :		str=3_0       ,			lowerCamelCase       :		List[str]=4_0_0       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Union[str, Any]=None       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		Optional[Any]=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		Any=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		str=True       ,			lowerCamelCase       :		Optional[Any]=None       ,			) ->							Union[str, Any]:
        _UpperCamelCase     :       Optional[int]  						=  size if size is not None else {'shortest_edge': 1_8}
        _UpperCamelCase     :       Dict  						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

        _UpperCamelCase     :       Any  						=  parent
        _UpperCamelCase     :       Optional[Any]  						=  batch_size
        _UpperCamelCase     :       List[Any]  						=  num_channels
        _UpperCamelCase     :       List[Any]  						=  num_frames
        _UpperCamelCase     :       Union[str, Any]  						=  image_size
        _UpperCamelCase     :       Dict  						=  min_resolution
        _UpperCamelCase     :       int  						=  max_resolution
        _UpperCamelCase     :       Union[str, Any]  						=  do_resize
        _UpperCamelCase     :       str  						=  size
        _UpperCamelCase     :       Dict  						=  do_normalize
        _UpperCamelCase     :       Union[str, Any]  						=  image_mean
        _UpperCamelCase     :       Optional[int]  						=  image_std
        _UpperCamelCase     :       Tuple  						=  do_center_crop
        _UpperCamelCase     :       Any  						=  crop_size

 def 					__snake_case					(			self       :		Tuple  ) ->							Optional[Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class    __lowerCAmelCase  (       a_,	unittest.TestCase						):



 '''simple docstring'''
 _lowerCamelCase						:	Any				    = TvltImageProcessor if is_vision_available() else None

 def 					__snake_case					(			self       :		int  ) ->							Optional[int]:
        _UpperCamelCase     :       Optional[Any]  						=  TvltImageProcessorTester(self  )

 @property
 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        return self.image_processor_tester.prepare_image_processor_dict()

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							str:
        _UpperCamelCase     :       Tuple  						=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'image_mean'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'image_std'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'do_normalize'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'do_resize'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'do_center_crop'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'size'  )  )

 def 					__snake_case					(			self       :		Dict  ) ->							Optional[int]:
        # Initialize image_processor
        _UpperCamelCase     :       str  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL videos
        _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			Image.Image  )

        # Test not batched input
        _UpperCamelCase     :       Dict  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)

        # Test batched
        _UpperCamelCase     :       Any  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							List[str]:
        # Initialize image_processor
        _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			numpify=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			np.ndarray  )

        # Test not batched input
        _UpperCamelCase     :       Optional[int]  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)

        # Test batched
        _UpperCamelCase     :       int  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        # Initialize image_processor
        _UpperCamelCase     :       int  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        _UpperCamelCase     :       Optional[Any]  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			torchify=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			torch.Tensor  )

        # Test not batched input
        _UpperCamelCase     :       Dict  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)

        # Test batched
        _UpperCamelCase     :       Optional[int]  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_videos.shape       ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )       ,			)



