


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()
lowerCAmelCase           =		logging.get_logger(__name__)







def        _lowerCamelCase(	lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			MobileNetVaConfig(layer_norm_eps=0.001	)

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

		__lowercase=			re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'   , lowercase__	)
		if matches:
				__lowercase=			float(matches[1]	)
				__lowercase=			int(matches[2]	)

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

		return config







def        _lowerCamelCase(	)						->	List[str]:

		'''simple docstring'''







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







@torch.no_grad()
def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__=False	)						->	Tuple:

		'''simple docstring'''







		__lowercase=			get_mobilenet_va_config(lowercase__	)

		# Load 🤗 model
		__lowercase=			MobileNetVaForImageClassification(lowercase__	).eval()

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

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

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

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase=			torch.tensor([-4.1739, -1.1233, 3.1205]	)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase=			torch.tensor([-3.9440, -2.3141, -0.3333]	)
		else:
				__lowercase=			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...'	)
				__lowercase=			'google/' + model_name
				image_processor.push_to_hub(lowercase__	)
				model.push_to_hub(lowercase__	)


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

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










import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(lowercase__   , lowercase__	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)
				_graph_replace_input_with(node_proto.attribute[1].g   , lowercase__   , lowercase__	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				__lowercase=			inits[i].name
				__lowercase=			inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

		__lowercase=			set()
		__lowercase=			{}
		__lowercase=			[]

		__lowercase=			0

		for i in range(len(lowercase__	)	):
				if i in dup_set:
						continue

				for j in range(i + 1   , len(lowercase__	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]   , inits[j]	):
								dup_set.add(lowercase__	)
								dup_set.add(lowercase__	)

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

								__lowercase=			inits[i].name
								__lowercase=			inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[name_j]
								ind_to_replace.append((j, i)	)

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






