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_			    =							logging.get_logger(__name__)




def       lowerCAmelCase_     (		lowercase:							List[Any]		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= MobileNetVaConfig(layer_norm_eps=0.001		)

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

 _UpperCamelCase:     int							= re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''  ,					lowercase		)
 if matches:
  _UpperCamelCase:     str							= float(matches[1]		)
  _UpperCamelCase:     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:     Optional[int]							= 1_001
 _UpperCamelCase:     Any							= '''imagenet-1k-id2label.json'''
 _UpperCamelCase:     Dict							= '''huggingface/label-files'''
 _UpperCamelCase:     Tuple							= json.load(open(hf_hub_download(lowercase  ,					lowercase  ,					repo_type='''dataset'''		)  ,					'''r'''		)		)
 _UpperCamelCase:     Union[str, Any]							= {int(lowercase		) + 1: v for k, v in idalabel.items()}
 _UpperCamelCase:     Dict							= '''background'''
 _UpperCamelCase:     Any							= idalabel
 _UpperCamelCase:     int							= {v: k for k, v in idalabel.items()}

 return config




def       lowerCAmelCase_     (		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     int							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:     Union[str, Any]							= Image.open(requests.get(lowercase  ,					stream=lowercase		).raw		)
 return im




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							List[str]  ,					lowercase:							List[Any]  ,					lowercase:							Optional[Any]=False		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     Any							= get_mobilenet_va_config(lowercase		)

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

 assert logits.shape == (1, 1_001)

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


if __name__ == "__main__":
  UpperCAmelCase_			    =							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_			    =							parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()