



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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor



class      _UpperCAmelCase      (							unittest.TestCase  ):




     def __init__(					self			,    a__			,    a__=1_3			,    a__=3			,    a__=2_2_4			,    a__=3_0			,    a__=4_0_0			,    a__=True			,    a__=None			,    a__=True			,    a__=[0.5, 0.5, 0.5]			,    a__=[0.5, 0.5, 0.5]			,    ):
         A__					=       size if size is not None else {'''height''': 1_8, '''width''': 1_8}
         A__					=       parent
         A__					=       batch_size
         A__					=       num_channels
         A__					=       image_size
         A__					=       min_resolution
         A__					=       max_resolution
         A__					=       do_resize
         A__					=       size
         A__					=       do_normalize
         A__					=       image_mean
         A__					=       image_std




     def 		snake_case_		(					self):
         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      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					ViTImageProcessor if is_vision_available() else None




     def 		snake_case_		(					self):
         A__					=       EfficientFormerImageProcessorTester(self)




     @property
     def 		snake_case_		(					self):
         return self.image_proc_tester.prepare_image_processor_dict()




     def 		snake_case_		(					self):
         A__					=       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 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         # Initialize image_processor
         A__					=       self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         A__					=       prepare_image_inputs(self.image_proc_tester			,    equal_resolution=a__)
         for image in image_inputs:
             self.assertIsInstance(a__			,    Image.Image)

         # Test not batched input
         A__					=       image_processor(image_inputs[0]			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )

         # Test batched
         A__					=       image_processor(a__			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )




     def 		snake_case_		(					self):
         # Initialize image_processor
         A__					=       self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         A__					=       prepare_image_inputs(self.image_proc_tester			,    equal_resolution=a__			,    numpify=a__)
         for image in image_inputs:
             self.assertIsInstance(a__			,    np.ndarray)

         # Test not batched input
         A__					=       image_processor(image_inputs[0]			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )

         # Test batched
         A__					=       image_processor(a__			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )




     def 		snake_case_		(					self):
         # Initialize image_processor
         A__					=       self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         A__					=       prepare_image_inputs(self.image_proc_tester			,    equal_resolution=a__			,    torchify=a__)
         for image in image_inputs:
             self.assertIsInstance(a__			,    torch.Tensor)

         # Test not batched input
         A__					=       image_processor(image_inputs[0]			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )

         # Test batched
         A__					=       image_processor(a__			,    return_tensors='''pt''').pixel_values
         self.assertEqual(
             encoded_images.shape			,    (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             )			,    )





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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    if matches:
        A__					=       float(matches[1]						)
        A__					=       int(matches[2]						)

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       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...'''						)
        A__					=       '''google/''' + model_name
        image_processor.push_to_hub(UpperCamelCase_						)
        model.push_to_hub(UpperCamelCase_						)


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

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