
"""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 					_snake_case (						snake_case__      :   Tuple   ):
	A        =      MobileNetVaConfig(layer_norm_eps=0.001   )

	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        =      1001
	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 					_snake_case (						):
	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 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   Tuple		,					snake_case__      :   str		,					snake_case__      :   int=False   ):
	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, 1001)

	if model_name == "mobilenet_v1_1.0_224":
		A        =      torch.tensor([-4.1739, -1.1233, 3.1205]   )
	elif model_name == "mobilenet_v1_0.75_192":
		A        =      torch.tensor([-3.9440, -2.3141, -0.3333]   )
	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__":
		_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,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    '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

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



