
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()
_A						      =				logging.get_logger(__name__)



def 		lowercase_    (     A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      MobileNetVaConfig(layer_norm_eps=0.001	)

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

    snake_case				  =      re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"				,	A__	)
    if matches:
        snake_case				  =      float(matches[1]	)
        snake_case				  =      int(matches[2]	)

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

    return config



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case				  =      Image.open(requests.get(A__				,	stream=A__	).raw	)
    return im



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__				,	A__=False	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      get_mobilenet_va_config(A__	)

    # Load 🤗 model
    snake_case				  =      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
    snake_case				  =      MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}				,	size={"shortest_edge": config.image_size + 32}				,	)
    snake_case				  =      image_processor(images=prepare_img()				,	return_tensors="pt"	)
    snake_case				  =      model(**A__	)
    snake_case				  =      outputs.logits

    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v1_1.0_224":
        snake_case				  =      torch.tensor([-4.1739, -1.1233, 3.1205]	)
    elif model_name == "mobilenet_v1_0.75_192":
        snake_case				  =      torch.tensor([-3.9440, -2.3141, -0.3333]	)
    else:
        snake_case				  =      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..."	)
        snake_case				  =      "google/" + model_name
        image_processor.push_to_hub(A__	)
        model.push_to_hub(A__	)


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

    _A						      =				parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Any     =	"poolformer"





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[Any]=3       ,					_A   :					Optional[int]=1_6       ,					_A   :					Dict=1_6       ,					_A   :					Tuple=3       ,					_A   :					Tuple=4.0       ,					_A   :					int=[2, 2, 6, 2]       ,					_A   :					Dict=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					_A   :					int=[7, 3, 3, 3]       ,					_A   :					List[str]=[4, 2, 2, 2]       ,					_A   :					str=[2, 1, 1, 1]       ,					_A   :					List[Any]=4       ,					_A   :					Any=0.0       ,					_A   :					Optional[Any]="gelu"       ,					_A   :					Optional[Any]=True       ,					_A   :					List[str]=1E-5       ,					_A   :					List[str]=0.02       ,					**_A   :					str       ,					)		->		Tuple:
         snake_case				  =      num_channels
         snake_case				  =      patch_size
         snake_case				  =      stride
         snake_case				  =      padding
         snake_case				  =      pool_size
         snake_case				  =      hidden_sizes
         snake_case				  =      mlp_ratio
         snake_case				  =      depths
         snake_case				  =      patch_sizes
         snake_case				  =      strides
         snake_case				  =      num_encoder_blocks
         snake_case				  =      drop_path_rate
         snake_case				  =      hidden_act
         snake_case				  =      use_layer_scale
         snake_case				  =      layer_scale_init_value
         snake_case				  =      initializer_range
         super().__init__(**_A     )







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	version.parse("1.11"						)





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]     )





     @property
     def 	UpperCAmelCase(self   :					int     )		->		float:
         return 2E-3

