
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_						:     int	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict  )   ->			int:






						'''simple docstring'''

						_lowercase       :	List[Any]           =   MobileNetVaConfig(layer_norm_eps=0.001  )

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

						_lowercase       :	Dict           =   re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" ,			_UpperCAmelCase  )
						if matches:
												_lowercase       :	str           =   float(matches[1]  )
												_lowercase       :	str           =   int(matches[2]  )

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

						return config



def 		UpperCamelCase     (       )   ->			List[Any]:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
						_lowercase       :	Union[str, Any]           =   Image.open(requests.get(_UpperCAmelCase ,			stream=_UpperCAmelCase  ).raw  )
						return im



@torch.no_grad()
def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   List[Any]=False  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	Any           =   get_mobilenet_va_config(_UpperCAmelCase  )

						# Load 🤗 model
						_lowercase       :	Optional[Any]           =   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
						_lowercase       :	List[str]           =   MobileNetVaImageProcessor(
						    crop_size={"width": config.image_size, "height": config.image_size} ,			size={"shortest_edge": config.image_size + 32} ,			)
						_lowercase       :	str           =   image_processor(images=prepare_img() ,			return_tensors="pt"  )
						_lowercase       :	Optional[Any]           =   model(**_UpperCAmelCase  )
						_lowercase       :	str           =   outputs.logits

						assert logits.shape == (1, 1001)

						if model_name == "mobilenet_v1_1.0_224":
												_lowercase       :	List[Any]           =   torch.tensor([-4.1_739, -1.1_233, 3.1_205]  )
						elif model_name == "mobilenet_v1_0.75_192":
												_lowercase       :	Optional[int]           =   torch.tensor([-3.9_440, -2.3_141, -0.3_333]  )
						else:
												_lowercase       :	int           =   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..."  )
												_lowercase       :	List[str]           =   "google/" + model_name
												image_processor.push_to_hub(_UpperCAmelCase  )
												model.push_to_hub(_UpperCAmelCase  )


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






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCamelCase_						:     Tuple	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: List[str]      ,	snake_case				: bool = True      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BILINEAR      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	**snake_case				: List[Any]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	List[str]           =   size if size is not None else {"shortest_edge": 256}
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	List[str]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	param_name="crop_size"    )
							_lowercase       :	List[Any]           =   do_resize
							_lowercase       :	Optional[Any]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Tuple           =   do_center_crop
							_lowercase       :	Any           =   crop_size
							_lowercase       :	str           =   do_rescale
							_lowercase       :	int           =   rescale_factor
							_lowercase       :	List[Any]           =   do_normalize
							_lowercase       :	List[str]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_lowercase       :	Optional[Any]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






	def       _a(self				: Tuple      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: int      ,	) ->			np.ndarray:
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							if "shortest_edge" not in size:
													raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
							_lowercase       :	int           =   get_resize_output_image_size(snake_case      ,	size=size["shortest_edge"]      ,	default_to_square=snake_case    )
							return resize(snake_case      ,	size=snake_case      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Union[str, Any]      ,	) ->			np.ndarray:
							_lowercase       :	Union[str, Any]           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: np.ndarray      ,	snake_case				: float      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[Any]    ) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: int      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Tuple      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Optional[int]      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: bool = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: Union[str, ChannelDimension] = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			Union[str, Any]:
							_lowercase       :	Any           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	List[str]           =   size if size is not None else self.size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Tuple           =   resample if resample is not None else self.resample
							_lowercase       :	List[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	int           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	param_name="crop_size"    )
							_lowercase       :	Dict           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	List[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	Union[str, Any]           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Optional[Any]           =   image_std if image_std is not None else self.image_std

							_lowercase       :	int           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."    )

							if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."    )

							if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."    )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."    )

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Tuple           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[str]           =   [self.resize(image=snake_case      ,	size=snake_case      ,	resample=snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	List[str]           =   [self.center_crop(image=snake_case      ,	size=snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	Any           =   [self.rescale(image=snake_case      ,	scale=snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	Optional[Any]           =   [self.normalize(image=snake_case      ,	mean=snake_case      ,	std=snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Optional[Any]           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )






	def       _a(self				: Dict      ,	snake_case				: List[str]      ,	snake_case				: List[Tuple] = None    ) ->			Optional[Any]:
							_lowercase       :	Dict           =   outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
													if len(snake_case    ) != len(snake_case    ):
																			raise ValueError(
																			    "Make sure that you pass in as many target sizes as the batch dimension of the logits"    )

													if is_torch_tensor(snake_case    ):
																			_lowercase       :	Dict           =   target_sizes.numpy()

													_lowercase       :	Tuple           =   []

													for idx in range(len(snake_case    )    ):
																			_lowercase       :	List[str]           =   torch.nn.functional.interpolate(
																			    logits[idx].unsqueeze(dim=0    )      ,	size=target_sizes[idx]      ,	mode="bilinear"      ,	align_corners=snake_case    )
																			_lowercase       :	int           =   resized_logits[0].argmax(dim=0    )
																			semantic_segmentation.append(snake_case    )
							else:
													_lowercase       :	Optional[int]           =   logits.argmax(dim=1    )
													_lowercase       :	int           =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

							return semantic_segmentation




