



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()
_lowercase:					Optional[int]      =						logging.get_logger(__name__)






def        a( A			:       str			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  MobileNetVaConfig(layer_norm_eps=0.001			)

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

					a   	=  re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"					,	lowercase__			)
					if matches:
										a   	=  float(matches[1]			)
										a   	=  int(matches[2]			)

					if model_name.startswith("deeplabv3_"			):
										a   	=  8
										a   	=  21
										a   	=  "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".
										a   	=  1001
										a   	=  "imagenet-1k-id2label.json"

					a   	=  "huggingface/label-files"
					a   	=  json.load(open(hf_hub_download(lowercase__					,	lowercase__					,	repo_type="dataset"			)					,	"r"			)			)

					if config.num_labels == 1001:
										a   	=  {int(lowercase__			) + 1: v for k, v in idalabel.items()}
										a   	=  "background"
					else:
										a   	=  {int(lowercase__			): v for k, v in idalabel.items()}

					a   	=  idalabel
					a   	=  {v: k for k, v in idalabel.items()}

					return config






def        a( )						->			int:
					"""simple docstring"""





					a   	=  "http://images.cocodataset.org/val2017/000000039769.jpg"
					a   	=  Image.open(requests.get(lowercase__					,	stream=lowercase__			).raw			)
					return im






@torch.no_grad()
def        a( A			:       Union[str, Any]					,	A			:       Any					,	A			:       str					,	A			:       Optional[Any]=False			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  get_mobilenet_va_config(lowercase__			)

					# Load 🤗 model
					if model_name.startswith("deeplabv3_"			):
										a   	=  MobileNetVaForSemanticSegmentation(lowercase__			).eval()
					else:
										a   	=  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
					a   	=  MobileNetVaImageProcessor(
					    crop_size={"width": config.image_size, "height": config.image_size}					,	size={"shortest_edge": config.image_size + 32}					,	)
					a   	=  image_processor(images=prepare_img()					,	return_tensors="pt"			)
					a   	=  model(**lowercase__			)
					a   	=  outputs.logits

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

										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
															a   	=  torch.tensor(
															    [
															        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
															        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
															        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
															    ]			)

										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":
															a   	=  torch.tensor([0.0_181, -1.0_015, 0.4_688]			)
										elif model_name == "mobilenet_v2_1.0_224":
															a   	=  torch.tensor([0.2_445, -1.1_993, 0.1_905]			)
										elif model_name == "mobilenet_v2_0.75_160":
															a   	=  torch.tensor([0.2_482, 0.4_136, 0.6_669]			)
										elif model_name == "mobilenet_v2_0.35_96":
															a   	=  torch.tensor([0.1_451, -0.4_624, 0.7_192]			)
										else:
															a   	=  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..."			)
										a   	=  "google/" + model_name
										image_processor.push_to_hub(lowercase__			)
										model.push_to_hub(lowercase__			)


if __name__ == "__main__":
				_lowercase:					Optional[Any]      =						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."
				)

				_lowercase:					List[str]      =						parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)



"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Any        =						[
	    'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CvtForImageClassification',
	    'CvtModel',
	    'CvtPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCvtForImageClassification',
	    'TFCvtModel',
	    'TFCvtPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cvt import (
		    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CvtForImageClassification,
		    CvtModel,
		    CvtPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_cvt import (
		    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCvtForImageClassification,
		    TFCvtModel,
		    TFCvtPreTrainedModel,
		)


else:
	import sys

	__A	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





