


"""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    :					Union[str, Any]							   =   logging.get_logger(__name__)







def 			__snake_case							( UpperCamelCase__						)   ->		Any:







		"""simple docstring"""





		A       							=			MobileNetVaConfig(layer_norm_eps=0.0_0_1						)

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

		A       							=			re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'			,      _A						)
		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(_A			,      _A			,      repo_type='dataset'						)			,      'r'						)						)
		A       							=			{int(_A						) + 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							( )   ->		Optional[int]:







		"""simple docstring"""





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







@torch.no_grad()
def 			__snake_case							( UpperCamelCase__			,      UpperCamelCase__			,      UpperCamelCase__			,      UpperCamelCase__=False						)   ->		Union[str, Any]:







		"""simple docstring"""





		A       							=			get_mobilenet_va_config(_A						)

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

		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v1_1.0_224":
				A       							=			torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]						)
		elif model_name == "mobilenet_v1_0.75_192":
				A       							=			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]						)
		else:
				A       							=			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...'						)
				A       							=			'google/' + model_name
				image_processor.push_to_hub(_A						)
				model.push_to_hub(_A						)


if __name__ == "__main__":
	UpperCamelCase    :					Any							   =   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    :					str							   =   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowercase        =    {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''albert'''



			def __init__(  self			,       __lowerCAmelCase=30000			,       __lowerCAmelCase=128			,       __lowerCAmelCase=4096			,       __lowerCAmelCase=12			,       __lowerCAmelCase=1			,       __lowerCAmelCase=64			,       __lowerCAmelCase=16384			,       __lowerCAmelCase=1			,       __lowerCAmelCase="gelu_new"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=0			,       __lowerCAmelCase=512			,       __lowerCAmelCase=2			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-1_2			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="absolute"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase			,       bos_token_id=__lowerCAmelCase			,       eos_token_id=__lowerCAmelCase			,       **__lowerCAmelCase)

					lowerCAmelCase     =    vocab_size
					lowerCAmelCase     =    embedding_size
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_hidden_groups
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    inner_group_num
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    max_position_embeddings
					lowerCAmelCase     =    type_vocab_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    position_embedding_type







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase     =    {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase     =    {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ])




