



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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase    ):
   A							:					Optional[Any]              =						MobileViTConfig()

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

   if mobilevit_name.startswith("deeplabv3_"    ):
      A							:					str              =						512
      A							:					Tuple              =						16
      A							:					str              =						21
      A							:					int              =						"pascal-voc-id2label.json"
   else:
      A							:					str              =						1000
      A							:					Optional[Any]              =						"imagenet-1k-id2label.json"

   A							:					Dict              =						"huggingface/label-files"
   A							:					List[Any]              =						json.load(open(hf_hub_download(__UpperCAmelCase ,    __UpperCAmelCase ,    repo_type="dataset"    ) ,    "r"    )    )
   A							:					List[Any]              =						{int(__UpperCAmelCase    ): v for k, v in idalabel.items()}
   A							:					Union[str, Any]              =						idalabel
   A							:					Tuple              =						{v: k for k, v in idalabel.items()}

   return config




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):







   for i in range(1 ,    6    ):
      if f"""layer_{i}.""" in name:
         A							:					List[Any]              =						name.replace(f"""layer_{i}.""" ,    f"""encoder.layer.{i - 1}."""    )

   if "conv_1." in name:
      A							:					int              =						name.replace("conv_1." ,    "conv_stem."    )
   if ".block." in name:
      A							:					str              =						name.replace(".block." ,    "."    )

   if "exp_1x1" in name:
      A							:					List[str]              =						name.replace("exp_1x1" ,    "expand_1x1"    )
   if "red_1x1" in name:
      A							:					Optional[Any]              =						name.replace("red_1x1" ,    "reduce_1x1"    )
   if ".local_rep.conv_3x3." in name:
      A							:					List[Any]              =						name.replace(".local_rep.conv_3x3." ,    ".conv_kxk."    )
   if ".local_rep.conv_1x1." in name:
      A							:					int              =						name.replace(".local_rep.conv_1x1." ,    ".conv_1x1."    )
   if ".norm." in name:
      A							:					Any              =						name.replace(".norm." ,    ".normalization."    )
   if ".conv." in name:
      A							:					Optional[int]              =						name.replace(".conv." ,    ".convolution."    )
   if ".conv_proj." in name:
      A							:					List[Any]              =						name.replace(".conv_proj." ,    ".conv_projection."    )

   for i in range(0 ,    2    ):
      for j in range(0 ,    4    ):
         if f""".{i}.{j}.""" in name:
            A							:					Optional[int]              =						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:
            A							:					List[str]              =						name.replace(f""".{i}.{j}.""" ,    f""".{i}."""    )
            if "expand_1x1" in name:
               A							:					int              =						name.replace("expand_1x1" ,    "downsampling_layer.expand_1x1"    )
            if "conv_3x3" in name:
               A							:					Any              =						name.replace("conv_3x3" ,    "downsampling_layer.conv_3x3"    )
            if "reduce_1x1" in name:
               A							:					Optional[int]              =						name.replace("reduce_1x1" ,    "downsampling_layer.reduce_1x1"    )

   for i in range(2 ,    5    ):
      if f""".global_rep.{i}.weight""" in name:
         A							:					Optional[Any]              =						name.replace(f""".global_rep.{i}.weight""" ,    ".layernorm.weight"    )
      if f""".global_rep.{i}.bias""" in name:
         A							:					str              =						name.replace(f""".global_rep.{i}.bias""" ,    ".layernorm.bias"    )

   if ".global_rep." in name:
      A							:					str              =						name.replace(".global_rep." ,    ".transformer."    )
   if ".pre_norm_mha.0." in name:
      A							:					int              =						name.replace(".pre_norm_mha.0." ,    ".layernorm_before."    )
   if ".pre_norm_mha.1.out_proj." in name:
      A							:					Union[str, Any]              =						name.replace(".pre_norm_mha.1.out_proj." ,    ".attention.output.dense."    )
   if ".pre_norm_ffn.0." in name:
      A							:					Optional[Any]              =						name.replace(".pre_norm_ffn.0." ,    ".layernorm_after."    )
   if ".pre_norm_ffn.1." in name:
      A							:					Dict              =						name.replace(".pre_norm_ffn.1." ,    ".intermediate.dense."    )
   if ".pre_norm_ffn.4." in name:
      A							:					Union[str, Any]              =						name.replace(".pre_norm_ffn.4." ,    ".output.dense."    )
   if ".transformer." in name:
      A							:					Any              =						name.replace(".transformer." ,    ".transformer.layer."    )

   if ".aspp_layer." in name:
      A							:					Optional[Any]              =						name.replace(".aspp_layer." ,    "."    )
   if ".aspp_pool." in name:
      A							:					List[Any]              =						name.replace(".aspp_pool." ,    "."    )
   if "seg_head." in name:
      A							:					List[str]              =						name.replace("seg_head." ,    "segmentation_head."    )
   if "segmentation_head.classifier.classifier." in name:
      A							:					Union[str, Any]              =						name.replace("segmentation_head.classifier.classifier." ,    "segmentation_head.classifier."    )

   if "classifier.fc." in name:
      A							:					List[Any]              =						name.replace("classifier.fc." ,    "classifier."    )
   elif (not base_model) and ("segmentation_head." not in name):
      A							:					Union[str, Any]              =						"mobilevit." + name

   return name




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=False    ):







   if base_model:
      A							:					Optional[Any]              =						""
   else:
      A							:					Any              =						"mobilevit."

   for key in orig_state_dict.copy().keys():
      A							:					Tuple              =						orig_state_dict.pop(__UpperCAmelCase    )

      if key[:8] == "encoder.":
         A							:					str              =						key[8:]

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

   return orig_state_dict




def 	UpperCAmelCase   (						):
   A							:					int              =						"http://images.cocodataset.org/val2017/000000039769.jpg"
   A							:					Dict              =						Image.open(requests.get(__UpperCAmelCase ,    stream=__UpperCAmelCase    ).raw    )
   return im




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=False    ):
   A							:					Optional[int]              =						get_mobilevit_config(__UpperCAmelCase    )

   # load original state_dict
   A							:					Optional[int]              =						torch.load(__UpperCAmelCase ,    map_location="cpu"    )

   # load 🤗 model
   if mobilevit_name.startswith("deeplabv3_"    ):
      A							:					Any              =						MobileViTForSemanticSegmentation(__UpperCAmelCase    ).eval()
   else:
      A							:					List[Any]              =						MobileViTForImageClassification(__UpperCAmelCase    ).eval()

   A							:					Union[str, Any]              =						convert_state_dict(__UpperCAmelCase ,    __UpperCAmelCase    )
   model.load_state_dict(__UpperCAmelCase    )

   # Check outputs on an image, prepared by MobileViTImageProcessor
   A							:					Tuple              =						MobileViTImageProcessor(crop_size=config.image_size ,    size=config.image_size + 32    )
   A							:					List[Any]              =						image_processor(images=prepare_img() ,    return_tensors="pt"    )
   A							:					Any              =						model(**__UpperCAmelCase    )
   A							:					Tuple              =						outputs.logits

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         A							:					List[str]              =						torch.tensor(
             [
                 [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                 [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                 [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
             ]    )
      elif mobilevit_name == "deeplabv3_mobilevit_xs":
         A							:					List[str]              =						torch.tensor(
             [
                 [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                 [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                 [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
             ]    )
      elif mobilevit_name == "deeplabv3_mobilevit_xxs":
         A							:					List[Any]              =						torch.tensor(
             [
                 [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                 [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.86_24, -9.59_64], [-10.8840, -10.8158, -10.6659]],
                 [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
             ]    )
      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":
         A							:					Union[str, Any]              =						torch.tensor([-0.98_66, 0.23_92, -1.12_41]    )
      elif mobilevit_name == "mobilevit_xs":
         A							:					Union[str, Any]              =						torch.tensor([-2.47_61, -0.93_99, -1.95_87]    )
      elif mobilevit_name == "mobilevit_xxs":
         A							:					Union[str, Any]              =						torch.tensor([-1.93_64, -1.23_27, -0.46_53]    )
      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:
      A							:					Dict              =						{
          "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..."    )
      A							:					Optional[Any]              =						model_mapping[mobilevit_name]
      image_processor.push_to_hub(__UpperCAmelCase ,    organization="apple"    )
      model.push_to_hub(__UpperCAmelCase ,    organization="apple"    )


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

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




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						else:
									raise ZeroDivisionError("Could not find root"    ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess    ) < precision:
									return next_guess

						A							:					int              =						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")