





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()
lowercase_         =						logging.get_logger(__name__)




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						)							->  Optional[int]:
  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__           =    0.0_5
    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(__snake_case						,		__snake_case						,		repo_type='dataset'						)						,		'r'						)						)
  lowercase__           =    {int(__snake_case						): v for k, v in idalabel.items()}
  lowercase__           =    idalabel
  lowercase__           =    {v: k for k, v in idalabel.items()}

  return config




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						)							->  Union[str, Any]:
  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      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						)							->  Tuple:
  if base_model:
    lowercase__           =    ""
  else:
    lowercase__           =    "mobilevit."

  for key in orig_state_dict.copy().keys():
    lowercase__           =    orig_state_dict.pop(__snake_case						)

    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      __UpperCamelCase    ()							->  int:
  lowercase__           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase__           =    Image.open(requests.get(__snake_case						,		stream=__snake_case						).raw						)
  return im




@torch.no_grad()
def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						)							->  Tuple:
  lowercase__           =    get_mobilevit_config(__snake_case						)

  # load original state_dict
  lowercase__           =    torch.load(__snake_case						,		map_location='cpu'						)

  # load 🤗 model
  if mobilevit_name.startswith('deeplabv3_'						):
    lowercase__           =    MobileViTForSemanticSegmentation(__snake_case						).eval()
  else:
    lowercase__           =    MobileViTForImageClassification(__snake_case						).eval()

  lowercase__           =    convert_state_dict(__snake_case						,		__snake_case						)
  model.load_state_dict(__snake_case						)

  # 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(**__snake_case						)
  lowercase__           =    outputs.logits

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      lowercase__           =    torch.tensor(
          [
              [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
              [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
              [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
          ]						)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      lowercase__           =    torch.tensor(
          [
              [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
              [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
              [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
          ]						)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      lowercase__           =    torch.tensor(
          [
              [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
              [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
              [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
          ]						)
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

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

    if mobilevit_name == "mobilevit_s":
      lowercase__           =    torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]						)
    elif mobilevit_name == "mobilevit_xs":
      lowercase__           =    torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]						)
    elif mobilevit_name == "mobilevit_xxs":
      lowercase__           =    torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]						)
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

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

  Path(__snake_case						).mkdir(exist_ok=__snake_case						)
  print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(__snake_case						)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
  image_processor.save_pretrained(__snake_case						)

  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(__snake_case						,		organization='apple'						)
    model.push_to_hub(__snake_case						,		organization='apple'						)


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

      lowercase_         =						parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )







"""simple docstring"""






from math import factorial



class      A__  :




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			real
				if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[1] * rank
				else:
							__lowerCAmelCase :    Optional[Any]				=			rank

	def __repr__(	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

				return (
				    F"""{self.real}+"""
				    F"""{'+'.join(str(_SCREAMING_SNAKE_CASE)+'E'+str(n+1)for n,dual in enumerate(self.duals))}"""
				)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.duals.copy()
				while cur[-1] == 0:
							cur.pop(-1)
				return Dual(self.real			,     _SCREAMING_SNAKE_CASE)

	def __add__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Union[str, Any]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							return Dual(self.real + other			,     self.duals)
				__lowerCAmelCase :    int				=			self.duals.copy()
				__lowerCAmelCase :    List[Any]				=			other.duals.copy()
				if len(_SCREAMING_SNAKE_CASE) > len(_SCREAMING_SNAKE_CASE):
							o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				elif len(_SCREAMING_SNAKE_CASE) < len(_SCREAMING_SNAKE_CASE):
							s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE) - len(_SCREAMING_SNAKE_CASE)))
				__lowerCAmelCase :    Tuple				=			[]
				for i in range(len(_SCREAMING_SNAKE_CASE)):
							new_duals.append(s_dual[i] + o_dual[i])
				return Dual(self.real + other.real			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __add__

	def __sub__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			Optional[Any]:

				"""simple docstring"""

				return self + other * -1

	def __mul__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[Any]				=			[]
							for i in self.duals:
										new_duals.append(i * other)
							return Dual(self.real * other			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			[0] * (len(self.duals) + len(other.duals) + 1)
				for i, item in enumerate(self.duals):
							for j, jtem in enumerate(other.duals):
										new_duals[i + j + 1] += item * jtem
				for k in range(len(self.duals)):
							new_duals[k] += self.duals[k] * other.real
				for index in range(len(other.duals)):
							new_duals[index] += other.duals[index] * self.real
				return Dual(self.real * other.real			,     _SCREAMING_SNAKE_CASE)

	SCREAMING_SNAKE_CASE        =      __mul__

	def __truediv__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						str)							->			Dict:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    List[str]				=			[]
							for i in self.duals:
										new_duals.append(i / other)
							return Dual(self.real / other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError

	def __floordiv__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			str:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    str				=			[]
							for i in self.duals:
										new_duals.append(i // other)
							return Dual(self.real // other			,     _SCREAMING_SNAKE_CASE)
				raise ValueError







	def __pow__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			List[Any]:

				"""simple docstring"""

				if n < 0 or isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							raise ValueError("power must be a positive integer")
				if n == 0:
							return 1
				if n == 1:
							return self
				__lowerCAmelCase :    List[Any]				=			self
				for _ in range(n - 1):
							x *= self
				return x






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not callable(__snake_case      ):
						raise ValueError("differentiate() requires a function as input for func"      )
			if not isinstance(__snake_case   ,(float, int)      ):
						raise ValueError("differentiate() requires a float as input for position"      )
			if not isinstance(__snake_case   ,__snake_case      ):
						raise ValueError("differentiate() requires an int as input for order"      )
			__lowerCAmelCase :    Any				=			Dual(__snake_case   ,1      )
			__lowerCAmelCase :    Tuple				=			func(__snake_case      )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


	def      _lowercase							(    __snake_case      )		->  Tuple:
				return y**2 * y**4





	print(differentiate(f, 9, 2))