


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()
lowerCAmelCase							=						logging.get_logger(__name__)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    Tuple:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  MobileNetVaConfig(layer_norm_eps=0.0_0_1			)

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

						__UpperCAmelCase :       List[Any]  						=  re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,					lowercase_			)
						if matches:
												__UpperCAmelCase :       Any  						=  float(matches[1]			)
												__UpperCAmelCase :       Optional[Any]  						=  int(matches[2]			)

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

						return config




def 						__SCREAMING_SNAKE_CASE       (					)		->    Dict:




						'''simple docstring'''







						__UpperCAmelCase :       Union[str, Any]  						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCAmelCase :       Tuple  						=  Image.open(requests.get(lowercase_			,					stream=lowercase_			).raw			)
						return im




@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_=False			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  get_mobilenet_va_config(lowercase_			)

						# Load 🤗 model
						__UpperCAmelCase :       int  						=  MobileNetVaForImageClassification(lowercase_			).eval()

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

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

						assert logits.shape == (1, 1001)

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

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

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

						if push_to_hub:
												print('''Pushing to the hub...'''			)
												__UpperCAmelCase :       List[str]  						=  '''google/''' + model_name
												image_processor.push_to_hub(lowercase_			)
												model.push_to_hub(lowercase_			)


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

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





from typing import TYPE_CHECKING

from ....utils import _LazyModule


lowerCAmelCase							=						{"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
				from .tokenization_tapex import TapexTokenizer


else:
				import sys

				lowerCAmelCase							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

