




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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case            = logging.get_logger(__name__)







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  str    =		MobileNetVaConfig(layer_norm_eps=0.0_01  )

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

      _A							:  Union[str, Any]    =		re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""",snake_case_  )
      if matches:
            _A							:  Dict    =		float(matches[1]  )
            _A							:  Union[str, Any]    =		int(matches[2]  )

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      _A							:  Optional[int]    =		1001
      _A							:  Union[str, Any]    =		"""imagenet-1k-id2label.json"""
      _A							:  int    =		"""huggingface/label-files"""
      _A							:  int    =		json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""  ),"""r"""  )  )
      _A							:  Tuple    =		{int(snake_case_  ) + 1: v for k, v in idalabel.items()}
      _A							:  Union[str, Any]    =		"""background"""
      _A							:  int    =		idalabel
      _A							:  Tuple    =		{v: k for k, v in idalabel.items()}

      return config







def        lowerCAmelCase_			(  ):
      _A							:  List[Any]    =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:  Union[str, Any]    =		Image.open(requests.get(snake_case_,stream=snake_case_  ).raw  )
      return im







@torch.no_grad()
def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_=False  ):
      _A							:  Dict    =		get_mobilenet_va_config(snake_case_  )

      # Load 🤗 model
      _A							:  Optional[Any]    =		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 MobileNetV1ImageProcessor
      _A							:  Union[str, Any]    =		MobileNetVaImageProcessor(
          crop_size={"""width""": config.image_size, """height""": config.image_size},size={"""shortest_edge""": config.image_size + 32},)
      _A							:  Any    =		image_processor(images=prepare_img(),return_tensors="""pt"""  )
      _A							:  Optional[int]    =		model(**snake_case_  )
      _A							:  List[str]    =		outputs.logits

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            _A							:  Any    =		torch.tensor([-4.17_39, -1.12_33, 3.12_05]  )
      elif model_name == "mobilenet_v1_0.75_192":
            _A							:  Any    =		torch.tensor([-3.94_40, -2.31_41, -0.33_33]  )
      else:
            _A							:  List[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..."""  )
            _A							:  Union[str, Any]    =		"""google/""" + model_name
            image_processor.push_to_hub(snake_case_  )
            model.push_to_hub(snake_case_  )


if __name__ == "__main__":
  _snake_case            = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="mobilenet_v1_1.0_224",
      type=str,
      help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."
  )

  _snake_case            = 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.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=3	,  _a=18	,  _a=30	,  _a=400	,  _a=True	,  _a=None	,  _a=True	,  _a=[0.5, 0.5, 0.5]	,  _a=[0.5, 0.5, 0.5]	,  )		->							Tuple:
           _A							:  Any    =		size if size is not None else {"""height""": 18, """width""": 18}
           _A							:  Optional[Any]    =		parent
           _A							:  Union[str, Any]    =		batch_size
           _A							:  List[Any]    =		num_channels
           _A							:  List[str]    =		image_size
           _A							:  Optional[Any]    =		min_resolution
           _A							:  List[Any]    =		max_resolution
           _A							:  Optional[Any]    =		do_resize
           _A							:  str    =		size
           _A							:  List[str]    =		do_normalize
           _A							:  Dict    =		image_mean
           _A							:  int    =		image_std



     def       a__			(						self       )		->							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,
           }







@require_torch
@require_vision
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		DPTImageProcessor if is_vision_available() else None

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Optional[Any]    =		DPTImageProcessingTester(self       )

     @property
     def       a__			(						self       )		->							Tuple:
           return self.image_processor_tester.prepare_image_processor_dict()

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(_a	,  """image_mean"""       )       )
           self.assertTrue(hasattr(_a	,  """image_std"""       )       )
           self.assertTrue(hasattr(_a	,  """do_normalize"""       )       )
           self.assertTrue(hasattr(_a	,  """do_resize"""       )       )
           self.assertTrue(hasattr(_a	,  """size"""       )       )

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size	,  {"""height""": 18, """width""": 18}       )

           _A							:  str    =		self.image_processing_class.from_dict(self.image_processor_dict	,  size=42       )
           self.assertEqual(image_processor.size	,  {"""height""": 42, """width""": 42}       )

     def       a__			(						self       )		->							List[Any]:
           # Initialize image_processing
           _A							:  Optional[int]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           _A							:  Any    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  Image.Image       )

           # Test not batched input
           _A							:  Union[str, Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

     def       a__			(						self       )		->							Union[str, Any]:
           # Initialize image_processing
           _A							:  List[Any]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           _A							:  Optional[Any]    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  numpify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  np.ndarray       )

           # Test not batched input
           _A							:  Dict    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  Any    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )



     def       a__			(						self       )		->							List[str]:
           # Initialize image_processing
           _A							:  Dict    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           _A							:  str    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  torchify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  torch.Tensor       )

           # Test not batched input
           _A							:  Optional[Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

