

'''simple docstring'''






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def __init__( self  :	Any   ,lowercase__  :	Tuple   ,lowercase__  :	bool = True   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	int = 3_2   ,lowercase__  :	bool = True   ,lowercase__  :	Union[int, float] = 1 / 2_5_5   ,lowercase__  :	bool = True   ,lowercase__  :	bool = True   ,lowercase__  :	Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,lowercase__  :	Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,lowercase__  :	bool = True   ,lowercase__  :	Any=7   ,lowercase__  :	Optional[int]=3_0   ,lowercase__  :	Tuple=4_0_0   ,lowercase__  :	List[Any]=3   ,):
   __lowercase				    =					parent
   __lowercase				    =					do_resize
   __lowercase				    =					size if size is not None else {'''shortest_edge''': 2_8_8}
   __lowercase				    =					size_divisor
   __lowercase				    =					do_rescale
   __lowercase				    =					rescale_factor
   __lowercase				    =					do_normalize
   __lowercase				    =					do_center_crop
   __lowercase				    =					image_mean
   __lowercase				    =					image_std
   __lowercase				    =					do_pad
   __lowercase				    =					batch_size
   __lowercase				    =					num_channels
   __lowercase				    =					min_resolution
   __lowercase				    =					max_resolution






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
       "size_divisor": self.size_divisor,
   }






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Optional[int]=False     ):
   if not batched:
    __lowercase				    =					self.size['''shortest_edge''']
    __lowercase				    =					image_inputs[0]
    if isinstance(lowercase__   ,Image.Image     ):
     __lowercase    ,      __lowercase				    =					image.size
    else:
     __lowercase    ,      __lowercase				    =					image.shape[1], image.shape[2]
    __lowercase				    =					size / min(lowercase__   ,lowercase__     )
    if h < w:
     __lowercase    ,      __lowercase				    =					size, scale * w
    else:
     __lowercase    ,      __lowercase				    =					scale * h, size

    __lowercase				    =					int((1_3_3_3 / 8_0_0) * size     )
    if max(lowercase__   ,lowercase__     ) > max_size:
     __lowercase				    =					max_size / max(lowercase__   ,lowercase__     )
     __lowercase				    =					newh * scale
     __lowercase				    =					neww * scale

    __lowercase    ,      __lowercase				    =					int(newh + 0.5     ), int(neww + 0.5     )
    __lowercase    ,      __lowercase				    =					(
        newh // self.size_divisor * self.size_divisor,
        neww // self.size_divisor * self.size_divisor,
    )

   else:
    __lowercase				    =					[]
    for image in image_inputs:
     __lowercase    ,      __lowercase				    =					self.get_expected_values([image]     )
     expected_values.append((expected_height, expected_width)     )
    __lowercase				    =					max(lowercase__   ,key=lambda lowercase__     : item[0]     )[0]
    __lowercase				    =					max(lowercase__   ,key=lambda lowercase__     : item[1]     )[1]

   return expected_height, expected_width






@require_torch
@require_vision
class      lowercase_      (lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     BridgeTowerImageProcessor if is_vision_available() else None






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					BridgeTowerImageProcessingTester(self     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return self.image_processor_tester.prepare_image_processor_dict()






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(lowercase__   ,'''image_mean'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''image_std'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''do_normalize'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''do_resize'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''size'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''size_divisor'''     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   # Initialize image processor
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,Image.Image     )

   # Test not batched input
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

   # Test batched
   __lowercase				    =					image_processing(lowercase__   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__   ,batched=lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   # Initialize image processor
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__   ,numpify=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,np.ndarray     )

   # Test not batched input
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

   # Test batched
   __lowercase				    =					image_processing(lowercase__   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__   ,batched=lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   # Initialize image processor
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__   ,torchify=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,torch.Tensor     )

   # Test not batched input
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

   # Test batched
   __lowercase				    =					image_processing(lowercase__   ,return_tensors='''pt'''     ).pixel_values

   __lowercase    ,      __lowercase				    =					self.image_processor_tester.get_expected_values(lowercase__   ,batched=lowercase__     )
   self.assertEqual(
       encoded_images.shape   ,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_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
   )
