'''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()
lowercase_     		= logging.get_logger(__name__)



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=MobileNetVaConfig(layer_norm_eps=0.001      )

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

						_lowerCAmelCase										=re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'			,      a__      )
						if matches:
												_lowerCAmelCase										=float(matches[1]      )
												_lowerCAmelCase										=int(matches[2]      )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						_lowerCAmelCase										=1_0_0_1
						_lowerCAmelCase										='imagenet-1k-id2label.json'
						_lowerCAmelCase										='huggingface/label-files'
						_lowerCAmelCase										=json.load(open(hf_hub_download(a__			,      a__			,      repo_type='dataset'      )			,      'r'      )      )
						_lowerCAmelCase										={int(a__      ) + 1: v for k, v in idalabel.items()}
						_lowerCAmelCase										='background'
						_lowerCAmelCase										=idalabel
						_lowerCAmelCase										={v: k for k, v in idalabel.items()}

						return config



def 							UpperCamelCase__			(			):


						'''simple docstring'''






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



@torch.no_grad()
def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=get_mobilenet_va_config(a__      )

						# Load 🤗 model
						_lowerCAmelCase										=MobileNetVaForImageClassification(a__      ).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						_lowerCAmelCase										=MobileNetVaImageProcessor(
						    crop_size={'width': config.image_size, 'height': config.image_size}			,      size={'shortest_edge': config.image_size + 3_2}			,      )
						_lowerCAmelCase										=image_processor(images=prepare_img()			,      return_tensors='pt'      )
						_lowerCAmelCase										=model(**a__      )
						_lowerCAmelCase										=outputs.logits

						assert logits.shape == (1, 1_0_0_1)

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

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

						Path(a__      ).mkdir(exist_ok=a__      )
						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
						model.save_pretrained(a__      )
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
						image_processor.save_pretrained(a__      )

						if push_to_hub:
												print('Pushing to the hub...'      )
												_lowerCAmelCase										='google/' + model_name
												image_processor.push_to_hub(a__      )
												model.push_to_hub(a__      )


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
							)






'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

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





