
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_ 						=						logging.get_logger(__name__)






def 		_UpperCAmelCase (						UpperCamelCase:  Optional[Any]	):






	"""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_([^_]*)_([^_]*)$"      ,				UpperCamelCase_	)
	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(UpperCamelCase_      ,				UpperCamelCase_      ,				repo_type="dataset"	)      ,				"r"	)	)
	__lowerCAmelCase    =				{int(UpperCamelCase_	) + 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(UpperCamelCase_      ,				stream=UpperCamelCase_	).raw	)
	return im






@torch.no_grad()
def 		_UpperCAmelCase (						UpperCamelCase:  Optional[int]      ,				UpperCamelCase:  int      ,				UpperCamelCase:  int      ,				UpperCamelCase:  Union[str, Any]=False	):






	"""simple docstring"""


	__lowerCAmelCase    =				get_mobilenet_va_config(UpperCamelCase_	)

	# Load 🤗 model
	__lowerCAmelCase    =				MobileNetVaForImageClassification(UpperCamelCase_	).eval()

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

	# 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(**UpperCamelCase_	)
	__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]      ,				UpperCamelCase_      ,				atol=1e-4	)

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

	if push_to_hub:
		print("Pushing to the hub..."	)
		__lowerCAmelCase    =				"google/" + model_name
		image_processor.push_to_hub(UpperCamelCase_	)
		model.push_to_hub(UpperCamelCase_	)


if __name__ == "__main__":
					UpperCamelCase_ 						=						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_ 						=						parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)










'''simple docstring'''
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class 					__lowerCAmelCase   (   unittest.TestCase				,			lowercase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		load_tool("text-classification"	)
													self.tool.setup()
													A_			    =		load_tool("text-classification" ,  remote=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.remote_tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.remote_tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)





