

'''simple docstring'''

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__			    =			logging.get_logger(__name__)




def       UpperCamelCase(     UpperCAmelCase_	):
 UpperCAmelCase       :	Tuple											= MobileNetVaConfig(layer_norm_eps=0.001	)

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

 UpperCAmelCase       :	Dict											= re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'  ,       SCREAMING_SNAKE_CASE__	)
 if matches:
  UpperCAmelCase       :	Tuple											= float(matches[1]	)
  UpperCAmelCase       :	int											= int(matches[2]	)

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 UpperCAmelCase       :	Any											= 10_01
 UpperCAmelCase       :	Tuple											= 'imagenet-1k-id2label.json'
 UpperCAmelCase       :	Dict											= 'huggingface/label-files'
 UpperCAmelCase       :	Union[str, Any]											= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       repo_type='dataset'	)  ,       'r'	)	)
 UpperCAmelCase       :	Union[str, Any]											= {int(SCREAMING_SNAKE_CASE__	) + 1: v for k, v in idalabel.items()}
 UpperCAmelCase       :	Dict											= 'background'
 UpperCAmelCase       :	Union[str, Any]											= idalabel
 UpperCAmelCase       :	Tuple											= {v: k for k, v in idalabel.items()}

 return config




def       UpperCamelCase(     ):
 UpperCAmelCase       :	Any											= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCAmelCase       :	List[Any]											= Image.open(requests.get(SCREAMING_SNAKE_CASE__  ,       stream=SCREAMING_SNAKE_CASE__	).raw	)
 return im




@torch.no_grad()
def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=False	):
 UpperCAmelCase       :	Tuple											= get_mobilenet_va_config(SCREAMING_SNAKE_CASE__	)

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

 assert logits.shape == (1, 10_01)

 if model_name == "mobilenet_v1_1.0_224":
  UpperCAmelCase       :	str											= torch.tensor([-4.1739, -1.1233, 3.1205]	)
 elif model_name == "mobilenet_v1_0.75_192":
  UpperCAmelCase       :	Optional[Any]											= torch.tensor([-3.9440, -2.3141, -0.3333]	)
 else:
  UpperCAmelCase       :	List[str]											= 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...'	)
  UpperCAmelCase       :	int											= 'google/' + model_name
  image_processor.push_to_hub(SCREAMING_SNAKE_CASE__	)
  model.push_to_hub(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 lowercase__			    =			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__			    =			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		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






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






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





