
"""simple docstring"""




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()
UpperCAmelCase__   :					Tuple												=				logging.get_logger(__name__)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileNetVaConfig(layer_norm_eps=0.001		)

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

							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""     ,_snake_case		)
							if matches:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     float(matches[1]		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     int(matches[2]		)

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

							return config

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

@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case=False		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     get_mobilenet_va_config(_snake_case		)

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

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

							if model_name == "mobilenet_v1_1.0_224":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.tensor([-4.1739, -1.1233, 3.1205]		)
							elif model_name == "mobilenet_v1_0.75_192":
														SCREAMING_SNAKE_CASE__		: str     =     torch.tensor([-3.9440, -2.3141, -0.3333]		)
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     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..."""		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     """google/""" + model_name
														image_processor.push_to_hub(_snake_case		)
														model.push_to_hub(_snake_case		)


if __name__ == "__main__":
					UpperCAmelCase__   :					int												=				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.'
					)

					UpperCAmelCase__   :					Dict												=				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_torch_available, is_vision_available


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




