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						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	MobileNetVaConfig(layer_norm_eps=0.001			)

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

    __UpperCamelCase  :Union[str, Any]    						=	re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''						,    SCREAMING_SNAKE_CASE			)
    if matches:
        __UpperCamelCase  :Union[str, Any]    						=	float(matches[1]			)
        __UpperCamelCase  :Tuple    						=	int(matches[2]			)

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    __UpperCamelCase  :List[Any]    						=	1_001
    __UpperCamelCase  :str    						=	'''imagenet-1k-id2label.json'''
    __UpperCamelCase  :Any    						=	'''huggingface/label-files'''
    __UpperCamelCase  :List[Any]    						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    repo_type='''dataset'''			)						,    '''r'''			)			)
    __UpperCamelCase  :Tuple    						=	{int(SCREAMING_SNAKE_CASE			) + 1: v for k, v in idalabel.items()}
    __UpperCamelCase  :Dict    						=	'''background'''
    __UpperCamelCase  :List[str]    						=	idalabel
    __UpperCamelCase  :int    						=	{v: k for k, v in idalabel.items()}

    return config



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase  :List[str]    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			)
    return im



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	get_mobilenet_va_config(SCREAMING_SNAKE_CASE			)

    # Load 🤗 model
    __UpperCamelCase  :Union[str, Any]    						=	MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE			).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    __UpperCamelCase  :Tuple    						=	MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}						,    size={'''shortest_edge''': config.image_size + 32}						,    )
    __UpperCamelCase  :List[Any]    						=	image_processor(images=prepare_img()						,    return_tensors='''pt'''			)
    __UpperCamelCase  :Union[str, Any]    						=	model(**SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Any    						=	outputs.logits

    assert logits.shape == (1, 1_001)

    if model_name == "mobilenet_v1_1.0_224":
        __UpperCamelCase  :int    						=	torch.tensor([-4.1_739, -1.1_233, 3.1_205]			)
    elif model_name == "mobilenet_v1_0.75_192":
        __UpperCamelCase  :List[Any]    						=	torch.tensor([-3.9_440, -2.3_141, -0.3_333]			)
    else:
        __UpperCamelCase  :Dict    						=	None

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

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

    if push_to_hub:
        print('''Pushing to the hub...'''			)
        __UpperCamelCase  :str    						=	'''google/''' + model_name
        image_processor.push_to_hub(SCREAMING_SNAKE_CASE			)
        model.push_to_hub(SCREAMING_SNAKE_CASE			)


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
    )






import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:


    class 			lowerCamelCase_  :






        '''simple docstring'''






        @staticmethod
        def     UpperCamelCase__     (			*__lowercase				,				**__lowercase) ->			Dict:
            pass


@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    a__			:		str      			=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

        __UpperCamelCase  :Dict    						=	[
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :Optional[Any]    						=	object_detector(examples[0]				,				threshold=0.0)

        __UpperCamelCase  :Dict    						=	len(__lowercase)
        self.assertGreater(__lowercase				,				0)
        self.assertEqual(
            __lowercase				,				[
                {
                    '''score''': ANY(__lowercase),
                    '''label''': ANY(__lowercase),
                    '''box''': {'''xmin''': ANY(__lowercase), '''ymin''': ANY(__lowercase), '''xmax''': ANY(__lowercase), '''ymax''': ANY(__lowercase)},
                }
                for i in range(__lowercase)
            ]				,				)







    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF''')
    def     UpperCamelCase__     (			self) ->			int:
        pass







    @require_torch
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Tuple    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

        __UpperCamelCase  :str    						=	object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ]				,				)

        __UpperCamelCase  :str    						=	object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ]				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                [
                    {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                    {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                    {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                ]
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :List[str]    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ]				,				)

        __UpperCamelCase  :Dict    						=	object_detector(
            [
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
            ]				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                [
                    {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
                [
                    {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
            ]				,				)







    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF''')
    def     UpperCamelCase__     (			self) ->			Dict:
        pass







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Tuple    						=	0.2
        __UpperCamelCase  :str    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=__lowercase				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Optional[int]    						=	2
        __UpperCamelCase  :Union[str, Any]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				top_k=__lowercase				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            ]				,				)





