

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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase							:  int     =				logging.get_logger(__name__)

def      a__	(						UpperCAmelCase				:     List[str]     )    ->		Any:
  UpperCAmelCase			:				List[Any]										=					MobileNetVaConfig(layer_norm_eps=0.001     )

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

  UpperCAmelCase			:				Any										=					re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''							,					UpperCAmelCase     )
  if matches:
    UpperCAmelCase			:				int										=					float(matches[1]     )
    UpperCAmelCase			:				str										=					int(matches[2]     )

  if model_name.startswith('''deeplabv3_'''     ):
    UpperCAmelCase			:				str										=					8
    UpperCAmelCase			:				Any										=					21
    UpperCAmelCase			:				Optional[Any]										=					'''pascal-voc-id2label.json'''
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    UpperCAmelCase			:				Union[str, Any]										=					1_001
    UpperCAmelCase			:				Union[str, Any]										=					'''imagenet-1k-id2label.json'''

  UpperCAmelCase			:				Union[str, Any]										=					'''huggingface/label-files'''
  UpperCAmelCase			:				Union[str, Any]										=					json.load(open(hf_hub_download(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )							,					'''r'''     )     )

  if config.num_labels == 1_001:
    UpperCAmelCase			:				List[str]										=					{int(UpperCAmelCase     ) + 1: v for k, v in idalabel.items()}
    UpperCAmelCase			:				str										=					'''background'''
  else:
    UpperCAmelCase			:				Optional[Any]										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}

  UpperCAmelCase			:				Dict										=					idalabel
  UpperCAmelCase			:				Optional[int]										=					{v: k for k, v in idalabel.items()}

  return config

def      a__	(						)    ->		Tuple:
  UpperCAmelCase			:				int										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  UpperCAmelCase			:				Union[str, Any]										=					Image.open(requests.get(UpperCAmelCase							,					stream=UpperCAmelCase     ).raw     )
  return im

@torch.no_grad()
def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     int=False     )    ->		str:
  UpperCAmelCase			:				int										=					get_mobilenet_va_config(UpperCAmelCase     )

  # Load 🤗 model
  if model_name.startswith('''deeplabv3_'''     ):
    UpperCAmelCase			:				Dict										=					MobileNetVaForSemanticSegmentation(UpperCAmelCase     ).eval()
  else:
    UpperCAmelCase			:				List[str]										=					MobileNetVaForImageClassification(UpperCAmelCase     ).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  UpperCAmelCase			:				int										=					MobileNetVaImageProcessor(
      crop_size={'''width''': config.image_size, '''height''': config.image_size}							,					size={'''shortest_edge''': config.image_size + 32}							,					)
  UpperCAmelCase			:				Tuple										=					image_processor(images=prepare_img()							,					return_tensors='''pt'''     )
  UpperCAmelCase			:				Dict										=					model(**UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					outputs.logits

  if model_name.startswith('''deeplabv3_'''     ):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      UpperCAmelCase			:				Tuple										=					torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]     )

    else:
      raise ValueError(f'''Unknown model name: {model_name}'''     )

    assert torch.allclose(logits[0, :3, :3, :3]							,					UpperCAmelCase							,					atol=1E-4     )
  else:
    assert logits.shape == (1, 1_001)

    if model_name == "mobilenet_v2_1.4_224":
      UpperCAmelCase			:				Tuple										=					torch.tensor([0.0181, -1.0015, 0.4688]     )
    elif model_name == "mobilenet_v2_1.0_224":
      UpperCAmelCase			:				Any										=					torch.tensor([0.2445, -1.1993, 0.1905]     )
    elif model_name == "mobilenet_v2_0.75_160":
      UpperCAmelCase			:				Any										=					torch.tensor([0.2482, 0.4136, 0.6669]     )
    elif model_name == "mobilenet_v2_0.35_96":
      UpperCAmelCase			:				List[Any]										=					torch.tensor([0.1451, -0.4624, 0.7192]     )
    else:
      UpperCAmelCase			:				Any										=					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...'''     )
    UpperCAmelCase			:				int										=					'''google/''' + model_name
    image_processor.push_to_hub(UpperCAmelCase     )
    model.push_to_hub(UpperCAmelCase     )


if __name__ == "__main__":
     _lowerCamelCase							:  Dict     =				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="mobilenet_v2_1.0_224",
         type=str,
         help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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							:  Optional[int]     =				parser.parse_args()
     convert_movilevit_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








import timeit





class    __UpperCAmelCase	:



   def __init__(				self				:	Dict,						__A				:	Union[str, Any]		):
     if isinstance(__A,						__A		):  # Initializes a matrix identical to the one provided.
       UpperCAmelCase			:				str										=					arg
       UpperCAmelCase			:				Optional[Any]										=					len(__A		)
     else:  # Initializes a square matrix of the given size and set values to zero.
       UpperCAmelCase			:				List[str]										=					arg
       UpperCAmelCase			:				List[Any]										=					[[0 for _ in range(self.n		)] for _ in range(self.n		)]





   def __mul__(				self				:	Dict,						__A				:	List[Any]		):
     UpperCAmelCase			:				Dict										=					Matrix(self.n		)
     for i in range(self.n		):
       for j in range(self.n		):
         for k in range(self.n		):
           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
     return matrix






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase			:				Tuple										=					Matrix([[1, 0], [0, 1]]     )
  while b > 0:
    if b & 1:
      matrix *= a
    a *= a
    b >>= 1
  return matrix

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa
  UpperCAmelCase			:				List[str]										=					Matrix([[1, 1], [1, 0]]     )
  UpperCAmelCase			:				Union[str, Any]										=					modular_exponentiation(UpperCAmelCase							,					n - 2     )
  return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		Optional[Any]:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa

  UpperCAmelCase			:				str										=					fa
  UpperCAmelCase			:				List[str]										=					fa
  n -= 2

  while n > 0:
    UpperCAmelCase					,		UpperCAmelCase			:				str										=					fn_a + fn_a, fn_a
    n -= 1

  return fn_a

def      a__	(						)    ->		Any:
  UpperCAmelCase			:				int										=					'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
  UpperCAmelCase			:				Dict										=					'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				Tuple										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print('''With matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		int:
  UpperCAmelCase			:				str										=					'''
from random import randint
from __main__ import simple_fibonacci
'''
  UpperCAmelCase			:				List[str]										=					'''simple_fibonacci(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				List[str]										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print(
      '''Without matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		List[str]:
  matrix_exponentiation_time()
  simple_fibonacci_time()


if __name__ == "__main__":
     main()





