





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       :Any       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

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

						SCREAMING_SNAKE_CASE__     :				Dict   =					re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"			,   _lowerCamelCase     )
						if matches:
												SCREAMING_SNAKE_CASE__     :				int   =					float(matches[1]     )
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					int(matches[2]     )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						SCREAMING_SNAKE_CASE__     :				Any   =					1_001
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE__     :				Dict   =					"huggingface/label-files"
						SCREAMING_SNAKE_CASE__     :				str   =					json.load(open(hf_hub_download(_lowerCamelCase			,   _lowerCamelCase			,   repo_type="dataset"     )			,   "r"     )     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{int(_lowerCamelCase     ) + 1: v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE__     :				int   =					"background"
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					idalabel
						SCREAMING_SNAKE_CASE__     :				Any   =					{v: k for k, v in idalabel.items()}

						return config







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE__     :				Tuple   =					Image.open(requests.get(_lowerCamelCase			,   stream=_lowerCamelCase     ).raw     )
						return im







@torch.no_grad()
def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  Dict=False     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					get_mobilenet_va_config(_lowerCamelCase     )

						# Load 🤗 model
						SCREAMING_SNAKE_CASE__     :				Any   =					MobileNetVaForImageClassification(_lowerCamelCase     ).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						SCREAMING_SNAKE_CASE__     :				Tuple   =					MobileNetVaImageProcessor(
						    crop_size={"width": config.image_size, "height": config.image_size}			,   size={"shortest_edge": config.image_size + 32}			,   )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processor(images=prepare_img()			,   return_tensors="pt"     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(**_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					outputs.logits

						assert logits.shape == (1, 1_001)

						if model_name == "mobilenet_v1_1.0_224":
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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":
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
						else:
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None

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

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

						if push_to_hub:
												print("Pushing to the hub..."     )
												SCREAMING_SNAKE_CASE__     :				Dict   =					"google/" + model_name
												image_processor.push_to_hub(_lowerCamelCase     )
												model.push_to_hub(_lowerCamelCase     )


if __name__ == "__main__":
				__lowercase       :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."
				)

				__lowercase       :Union[str, Any]       =  parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int | float]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) == 0:
												raise ValueError("find_max() arg is an empty sequence"     )
						if (
						    left >= len(_lowerCamelCase     )
						    or left < -len(_lowerCamelCase     )
						    or right >= len(_lowerCamelCase     )
						    or right < -len(_lowerCamelCase     )
						):
												raise IndexError("list index out of range"     )
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					find_max(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )  # find max in range[left, mid]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					find_max(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )  # find max in range[mid + 1, right]

						return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)