


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__  							=logging.get_logger(__name__)


def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	MobileViTConfig()

 # size of the architecture
 if "mobilevit_s" in mobilevit_name:
  __lowercase									=	[144, 192, 240]
  __lowercase									=	[16, 32, 64, 96, 128, 160, 640]
 elif "mobilevit_xs" in mobilevit_name:
  __lowercase									=	[96, 120, 144]
  __lowercase									=	[16, 32, 48, 64, 80, 96, 384]
 elif "mobilevit_xxs" in mobilevit_name:
  __lowercase									=	[64, 80, 96]
  __lowercase									=	[16, 16, 24, 48, 64, 80, 320]
  __lowercase									=	0.05
  __lowercase									=	2.0

 if mobilevit_name.startswith("""deeplabv3_""" ):
  __lowercase									=	512
  __lowercase									=	16
  __lowercase									=	21
  __lowercase									=	"""pascal-voc-id2label.json"""
 else:
  __lowercase									=	1000
  __lowercase									=	"""imagenet-1k-id2label.json"""

 __lowercase									=	"""huggingface/label-files"""
 __lowercase									=	json.load(open(hf_hub_download(UpperCamelCase__				,   UpperCamelCase__				,   repo_type="""dataset""" )				,   """r""" ) )
 __lowercase									=	{int(UpperCamelCase__ ): v for k, v in idalabel.items()}
 __lowercase									=	idalabel
 __lowercase									=	{v: k for k, v in idalabel.items()}

 return config


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: List[Any]=False ):






 """simple docstring"""







 for i in range(1				,   6 ):
  if f'''layer_{i}.''' in name:
   __lowercase									=	name.replace(f'''layer_{i}.'''				,   f'''encoder.layer.{i - 1}.''' )

 if "conv_1." in name:
  __lowercase									=	name.replace("""conv_1."""				,   """conv_stem.""" )
 if ".block." in name:
  __lowercase									=	name.replace(""".block."""				,   """.""" )

 if "exp_1x1" in name:
  __lowercase									=	name.replace("""exp_1x1"""				,   """expand_1x1""" )
 if "red_1x1" in name:
  __lowercase									=	name.replace("""red_1x1"""				,   """reduce_1x1""" )
 if ".local_rep.conv_3x3." in name:
  __lowercase									=	name.replace(""".local_rep.conv_3x3."""				,   """.conv_kxk.""" )
 if ".local_rep.conv_1x1." in name:
  __lowercase									=	name.replace(""".local_rep.conv_1x1."""				,   """.conv_1x1.""" )
 if ".norm." in name:
  __lowercase									=	name.replace(""".norm."""				,   """.normalization.""" )
 if ".conv." in name:
  __lowercase									=	name.replace(""".conv."""				,   """.convolution.""" )
 if ".conv_proj." in name:
  __lowercase									=	name.replace(""".conv_proj."""				,   """.conv_projection.""" )

 for i in range(0				,   2 ):
  for j in range(0				,   4 ):
   if f'''.{i}.{j}.''' in name:
    __lowercase									=	name.replace(f'''.{i}.{j}.'''				,   f'''.{i}.layer.{j}.''' )

 for i in range(2				,   6 ):
  for j in range(0				,   4 ):
   if f'''.{i}.{j}.''' in name:
    __lowercase									=	name.replace(f'''.{i}.{j}.'''				,   f'''.{i}.''' )
    if "expand_1x1" in name:
     __lowercase									=	name.replace("""expand_1x1"""				,   """downsampling_layer.expand_1x1""" )
    if "conv_3x3" in name:
     __lowercase									=	name.replace("""conv_3x3"""				,   """downsampling_layer.conv_3x3""" )
    if "reduce_1x1" in name:
     __lowercase									=	name.replace("""reduce_1x1"""				,   """downsampling_layer.reduce_1x1""" )

 for i in range(2				,   5 ):
  if f'''.global_rep.{i}.weight''' in name:
   __lowercase									=	name.replace(f'''.global_rep.{i}.weight'''				,   """.layernorm.weight""" )
  if f'''.global_rep.{i}.bias''' in name:
   __lowercase									=	name.replace(f'''.global_rep.{i}.bias'''				,   """.layernorm.bias""" )

 if ".global_rep." in name:
  __lowercase									=	name.replace(""".global_rep."""				,   """.transformer.""" )
 if ".pre_norm_mha.0." in name:
  __lowercase									=	name.replace(""".pre_norm_mha.0."""				,   """.layernorm_before.""" )
 if ".pre_norm_mha.1.out_proj." in name:
  __lowercase									=	name.replace(""".pre_norm_mha.1.out_proj."""				,   """.attention.output.dense.""" )
 if ".pre_norm_ffn.0." in name:
  __lowercase									=	name.replace(""".pre_norm_ffn.0."""				,   """.layernorm_after.""" )
 if ".pre_norm_ffn.1." in name:
  __lowercase									=	name.replace(""".pre_norm_ffn.1."""				,   """.intermediate.dense.""" )
 if ".pre_norm_ffn.4." in name:
  __lowercase									=	name.replace(""".pre_norm_ffn.4."""				,   """.output.dense.""" )
 if ".transformer." in name:
  __lowercase									=	name.replace(""".transformer."""				,   """.transformer.layer.""" )

 if ".aspp_layer." in name:
  __lowercase									=	name.replace(""".aspp_layer."""				,   """.""" )
 if ".aspp_pool." in name:
  __lowercase									=	name.replace(""".aspp_pool."""				,   """.""" )
 if "seg_head." in name:
  __lowercase									=	name.replace("""seg_head."""				,   """segmentation_head.""" )
 if "segmentation_head.classifier.classifier." in name:
  __lowercase									=	name.replace("""segmentation_head.classifier.classifier."""				,   """segmentation_head.classifier.""" )

 if "classifier.fc." in name:
  __lowercase									=	name.replace("""classifier.fc."""				,   """classifier.""" )
 elif (not base_model) and ("segmentation_head." not in name):
  __lowercase									=	"""mobilevit.""" + name

 return name


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Any=False ):






 """simple docstring"""







 if base_model:
  __lowercase									=	""""""
 else:
  __lowercase									=	"""mobilevit."""

 for key in orig_state_dict.copy().keys():
  __lowercase									=	orig_state_dict.pop(UpperCamelCase__ )

  if key[:8] == "encoder.":
   __lowercase									=	key[8:]

  if "qkv" in key:
   __lowercase									=	key.split(""".""" )
   __lowercase									=	int(key_split[0][6:] ) - 1
   __lowercase									=	int(key_split[3] )
   __lowercase									=	model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}''' )
   __lowercase									=	layer.transformer.layer[transformer_num].attention.attention.all_head_size
   __lowercase									=	(
       f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
   )
   if "weight" in key:
    __lowercase									=	val[:dim, :]
    __lowercase									=	val[dim : dim * 2, :]
    __lowercase									=	val[-dim:, :]
   else:
    __lowercase									=	val[:dim]
    __lowercase									=	val[dim : dim * 2]
    __lowercase									=	val[-dim:]
  else:
   __lowercase									=	val

 return orig_state_dict


def lowerCAmelCase_      (				):






 """simple docstring"""







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


@torch.no_grad()
def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: Tuple				,   UpperCamelCase__	: int				,   UpperCamelCase__	: List[Any]=False ):






 """simple docstring"""







 __lowercase									=	get_mobilevit_config(UpperCamelCase__ )

 # load original state_dict
 __lowercase									=	torch.load(UpperCamelCase__				,   map_location="""cpu""" )

 # load 🤗 model
 if mobilevit_name.startswith("""deeplabv3_""" ):
  __lowercase									=	MobileViTForSemanticSegmentation(UpperCamelCase__ ).eval()
 else:
  __lowercase									=	MobileViTForImageClassification(UpperCamelCase__ ).eval()

 __lowercase									=	convert_state_dict(UpperCamelCase__				,   UpperCamelCase__ )
 model.load_state_dict(UpperCamelCase__ )

 # Check outputs on an image, prepared by MobileViTImageProcessor
 __lowercase									=	MobileViTImageProcessor(crop_size=config.image_size				,   size=config.image_size + 32 )
 __lowercase									=	image_processor(images=prepare_img()				,   return_tensors="""pt""" )
 __lowercase									=	model(**UpperCamelCase__ )
 __lowercase									=	outputs.logits

 if mobilevit_name.startswith("""deeplabv3_""" ):
  assert logits.shape == (1, 21, 32, 32)

  if mobilevit_name == "deeplabv3_mobilevit_s":
   __lowercase									=	torch.tensor(
       [
           [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
           [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
           [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
       ] )
  elif mobilevit_name == "deeplabv3_mobilevit_xs":
   __lowercase									=	torch.tensor(
       [
           [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
           [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
           [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
       ] )
  elif mobilevit_name == "deeplabv3_mobilevit_xxs":
   __lowercase									=	torch.tensor(
       [
           [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
           [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
           [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
       ] )
  else:
   raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

  assert torch.allclose(logits[0, :3, :3, :3]				,   UpperCamelCase__				,   atol=1E-4 )
 else:
  assert logits.shape == (1, 1000)

  if mobilevit_name == "mobilevit_s":
   __lowercase									=	torch.tensor([-0.9866, 0.2392, -1.1241] )
  elif mobilevit_name == "mobilevit_xs":
   __lowercase									=	torch.tensor([-2.4761, -0.9399, -1.9587] )
  elif mobilevit_name == "mobilevit_xxs":
   __lowercase									=	torch.tensor([-1.9364, -1.2327, -0.4653] )
  else:
   raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}''' )

  assert torch.allclose(logits[0, :3]				,   UpperCamelCase__				,   atol=1E-4 )

 Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
 print(f'''Saving model {mobilevit_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:
  __lowercase									=	{
      """mobilevit_s""": """mobilevit-small""",
      """mobilevit_xs""": """mobilevit-x-small""",
      """mobilevit_xxs""": """mobilevit-xx-small""",
      """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
      """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
      """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
  }

  print("""Pushing to the hub...""" )
  __lowercase									=	model_mapping[mobilevit_name]
  image_processor.push_to_hub(UpperCamelCase__				,   organization="""apple""" )
  model.push_to_hub(UpperCamelCase__				,   organization="""apple""" )


if __name__ == "__main__":
       UpperCAmelCase__  							=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--mobilevit_name",
           default="mobilevit_s",
           type=str,
           help=(
               "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
               " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
           ),
       )
       parser.add_argument(
           "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt 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.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 if n == 0:
  return 0
 __lowercase									=	float("""-inf""" )
 for i in range(1				,   n + 1 ):
  __lowercase									=	max(
      UpperCamelCase__				,   prices[i - 1] + naive_cut_rod_recursive(n - i				,   UpperCamelCase__ ) )

 return max_revue


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 return _top_down_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowercase									=	float("""-inf""" )
  for i in range(1				,   n + 1 ):
   __lowercase									=	max(
       UpperCamelCase__				,   prices[i - 1] + _top_down_cut_rod_recursive(n - i				,   UpperCamelCase__				,   UpperCamelCase__ )				,   )

  __lowercase									=	max_revenue

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 _enforce_args(UpperCamelCase__				,   UpperCamelCase__ )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowercase									=	[float("""-inf""" ) for _ in range(n + 1 )]
 __lowercase									=	0

 for i in range(1				,   n + 1 ):
  __lowercase									=	max_rev[i]
  for j in range(1				,   i + 1 ):
   __lowercase									=	max(UpperCamelCase__				,   prices[j - 1] + max_rev[i - j] )

  __lowercase									=	max_revenue_i

 return max_rev[n]


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: list ):






 """simple docstring"""







 if n < 0:
  __lowercase									=	f'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(UpperCamelCase__ )

 if n > len(UpperCamelCase__ ):
  __lowercase									=	(
      """Each integral piece of rod must have a corresponding price. """
      f'''Got n = {n} but length of prices = {len(UpperCamelCase__ )}'''
  )
  raise ValueError(UpperCamelCase__ )


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[6, 10, 12, 15, 20, 23]
 __lowercase									=	len(UpperCamelCase__ )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowercase									=	36

 __lowercase									=	top_down_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	bottom_up_cut_rod(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	naive_cut_rod_recursive(UpperCamelCase__				,   UpperCamelCase__ )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()
