



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     int
						_snake_case			:     int
						_snake_case			:     float
						_snake_case			:     float
						_snake_case			:     Tuple[int]



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape	) == len(self.y.shape	) == len(self.z.shape	) == len(self.origin.shape	) == 2



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return torch.from_numpy(np.array([self.width, self.height]							,      dtype=np.floataa	)	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,      dtype=np.floataa	)	)



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			torch.arange(self.height * self.width	)
							__lowercase		       =			torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase							,      self.width							,      rounding_mode="trunc"	),
							    ]							,      axis=1							,      )
							return coords



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase       ,    *__lowercase		       =			self.shape
							__lowercase		       =			int(np.prod(lowerCamelCase	)	)

							__lowercase		       =			self.get_image_coords()
							__lowercase		       =			torch.broadcast_to(coords.unsqueeze(0	)							,      [batch_size * inner_batch_size, *coords.shape]	)
							__lowercase		       =			self.get_camera_rays(lowerCamelCase	)

							__lowercase		       =			rays.view(lowerCamelCase							,      inner_batch_size * self.height * self.width							,      2							,      3	)

							return rays



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							torch.Tensor	):






							'''simple docstring'''



							__lowercase       ,    *__lowercase       ,    __lowercase		       =			coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowercase		       =			coords.view(lowerCamelCase							,      -1							,      2	)

							__lowercase		       =			self.resolution()
							__lowercase		       =			self.fov()

							__lowercase		       =			(flat.float() / (res - 1)) * 2 - 1
							__lowercase		       =			fracs * torch.tan(fov / 2	)

							__lowercase		       =			fracs.view(lowerCamelCase							,      -1							,      2	)
							__lowercase		       =			(
							    self.z.view(lowerCamelCase							,      1							,      3	)
							    + self.x.view(lowerCamelCase							,      1							,      3	) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase							,      1							,      3	) * fracs[:, :, 1:]
							)
							__lowercase		       =			directions / directions.norm(dim=-1							,      keepdim=lowerCamelCase	)
							__lowercase		       =			torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase							,      1							,      3	)							,      [batch_size, directions.shape[1], 3]	),
							        directions,
							    ]							,      dim=2							,      )
							return rays.view(lowerCamelCase							,      *lowerCamelCase							,      2							,      3	)



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin							,      x=self.x							,      y=self.y							,      z=self.z							,      width=lowerCamelCase							,      height=lowerCamelCase							,      x_fov=self.x_fov							,      y_fov=self.y_fov							,      )



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]
	for theta in np.linspace(0  ,	2 * np.pi  ,	num=2_0   ):
		__lowercase		       =			np.array([np.sin(_SCREAMING_SNAKE_CASE   ), np.cos(_SCREAMING_SNAKE_CASE   ), -0.5]   )
		z /= np.sqrt(np.sum(z**2   )   )
		__lowercase		       =			-z * 4
		__lowercase		       =			np.array([np.cos(_SCREAMING_SNAKE_CASE   ), -np.sin(_SCREAMING_SNAKE_CASE   ), 0.0]   )
		__lowercase		       =			np.cross(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		origins.append(_SCREAMING_SNAKE_CASE   )
		xs.append(_SCREAMING_SNAKE_CASE   )
		ys.append(_SCREAMING_SNAKE_CASE   )
		zs.append(_SCREAMING_SNAKE_CASE   )
	return DifferentiableProjectiveCamera(
	    origin=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	x=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	y=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	z=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	width=_SCREAMING_SNAKE_CASE  ,	height=_SCREAMING_SNAKE_CASE  ,	x_fov=0.7  ,	y_fov=0.7  ,	shape=(1, len(_SCREAMING_SNAKE_CASE   ))  ,	)








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()
snake_case__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

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

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

	# 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(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

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

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			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":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			None

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

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

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


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



