






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()
__A      =				logging.get_logger(__name__)




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

					_A								= re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''					,			__snake_case )
					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								= 10_01
					_A								= '''imagenet-1k-id2label.json'''
					_A								= '''huggingface/label-files'''
					_A								= json.load(open(hf_hub_download(__snake_case					,			__snake_case					,			repo_type='''dataset''' )					,			'''r''' ) )
					_A								= {int(__snake_case ) + 1: v for k, v in idalabel.items()}
					_A								= '''background'''
					_A								= idalabel
					_A								= {v: k for k, v in idalabel.items()}

					return config




def      __A					(     ):

					'''simple docstring'''







					_A								= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_A								= Image.open(requests.get(__snake_case					,			stream=__snake_case ).raw )
					return im




@torch.no_grad()
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=False ):

					'''simple docstring'''







					_A								= get_mobilenet_va_config(__snake_case )

					# Load 🤗 model
					_A								= 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
					_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(**__snake_case )
					_A								= outputs.logits

					assert logits.shape == (1, 10_01)

					if model_name == "mobilenet_v1_1.0_224":
										_A								= torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
					elif model_name == "mobilenet_v1_0.75_192":
										_A								= torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
					else:
										_A								= 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...''' )
										_A								= '''google/''' + model_name
										image_processor.push_to_hub(__snake_case )
										model.push_to_hub(__snake_case )


if __name__ == "__main__":
				__A      =				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.'
				)

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

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



