





import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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





def     _UpperCamelCase     (a__   :Union[str, Any]       ):






							"""simple docstring"""





							print("""Loading config file..."""       )

							def flatten_yaml_as_dict(a__   :List[str]   ,			a__   :Tuple=""   ,			a__   :str="."       ):
														UpperCamelCase__				     =			[]
														for k, v in d.items():
																					UpperCamelCase__				     =			parent_key + sep + k if parent_key else k
																					if isinstance(a__   ,			collections.abc.MutableMapping       ):
																												items.extend(flatten_yaml_as_dict(a__   ,			a__   ,			sep=a__       ).items()       )
																					else:
																												items.append((new_key, v)       )
														return dict(a__       )

							UpperCamelCase__				     =			argparse.Namespace()
							with open(a__   ,			"""r"""       ) as yaml_file:
														try:
																					UpperCamelCase__				     =			yaml.load(a__   ,			Loader=yaml.FullLoader       )

																					UpperCamelCase__				     =			flatten_yaml_as_dict(a__       )
																					for k, v in flat_cfg.items():
																												setattr(a__   ,			a__   ,			a__       )
														except yaml.YAMLError as exc:
																					logger.error("""Error while loading config file: {}. Error message: {}""".format(a__   ,			str(a__       )       )       )
							return config





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			MobileViTVaConfig()

							UpperCamelCase__				     =			False

							# dataset
							if task_name.startswith("""imagenet1k_"""       ):
														UpperCamelCase__				     =			1000
														if int(task_name.strip().split("""_"""       )[-1]       ) == 384:
																					UpperCamelCase__				     =			384
														else:
																					UpperCamelCase__				     =			256
														UpperCamelCase__				     =			"""imagenet-1k-id2label.json"""
							elif task_name.startswith("""imagenet21k_to_1k_"""       ):
														UpperCamelCase__				     =			2_1000
														if int(task_name.strip().split("""_"""       )[-1]       ) == 384:
																					UpperCamelCase__				     =			384
														else:
																					UpperCamelCase__				     =			256
														UpperCamelCase__				     =			"""imagenet-22k-id2label.json"""
							elif task_name.startswith("""ade20k_"""       ):
														UpperCamelCase__				     =			151
														UpperCamelCase__				     =			512
														UpperCamelCase__				     =			"""ade20k-id2label.json"""
														UpperCamelCase__				     =			True
							elif task_name.startswith("""voc_"""       ):
														UpperCamelCase__				     =			21
														UpperCamelCase__				     =			512
														UpperCamelCase__				     =			"""pascal-voc-id2label.json"""
														UpperCamelCase__				     =			True

							# orig_config
							UpperCamelCase__				     =			load_orig_config_file(a__       )
							assert getattr(a__   ,			"""model.classification.name"""   ,			-1       ) == "mobilevit_v2", "Invalid model"
							UpperCamelCase__				     =			getattr(a__   ,			"""model.classification.mitv2.width_multiplier"""   ,			1.0       )
							assert (
							    getattr(a__   ,			"""model.classification.mitv2.attn_norm_layer"""   ,			-1       ) == "layer_norm_2d"
							), "Norm layers other than layer_norm_2d is not supported"
							UpperCamelCase__				     =			getattr(a__   ,			"""model.classification.activation.name"""   ,			"""swish"""       )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														UpperCamelCase__				     =			getattr(a__   ,			"""model.segmentation.output_stride"""   ,			16       )
														if "_deeplabv3" in task_name:
																					UpperCamelCase__				     =			getattr(a__   ,			"""model.segmentation.deeplabv3.aspp_rates"""   ,			[12, 24, 36]       )
																					UpperCamelCase__				     =			getattr(a__   ,			"""model.segmentation.deeplabv3.aspp_out_channels"""   ,			512       )
																					UpperCamelCase__				     =			getattr(a__   ,			"""model.segmentation.deeplabv3.aspp_dropout"""   ,			0.1       )

    # id2label
							UpperCamelCase__				     =			"""huggingface/label-files"""
							UpperCamelCase__				     =			json.load(open(hf_hub_download(a__   ,			a__   ,			repo_type="""dataset"""       )   ,			"""r"""       )       )
							UpperCamelCase__				     =			{int(a__       ): v for k, v in idalabel.items()}
							UpperCamelCase__				     =			idalabel
							UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}

							return config





def     _UpperCamelCase     (a__   :str   ,			a__   :Optional[Any]   ,			a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			dct.pop(a__       )
							UpperCamelCase__				     =			val





def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :Dict=False       ):






							"""simple docstring"""





							if base_model:
														UpperCamelCase__				     =			""""""
							else:
														UpperCamelCase__				     =			"""mobilevitv2."""

							UpperCamelCase__				     =			[]
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					UpperCamelCase__				     =			k[8:]
														else:
																					UpperCamelCase__				     =			k

														if ".block." in k:
																					UpperCamelCase__				     =			k_new.replace(""".block."""   ,			"""."""       )
														if ".conv." in k:
																					UpperCamelCase__				     =			k_new.replace(""".conv."""   ,			""".convolution."""       )
														if ".norm." in k:
																					UpperCamelCase__				     =			k_new.replace(""".norm."""   ,			""".normalization."""       )

														if "conv_1." in k:
																					UpperCamelCase__				     =			k_new.replace("""conv_1."""   ,			f"""{model_prefix}conv_stem."""       )
														for i in [1, 2]:
																					if f"""layer_{i}.""" in k:
																												UpperCamelCase__				     =			k_new.replace(f"""layer_{i}."""   ,			f"""{model_prefix}encoder.layer.{i-1}.layer."""       )
														if ".exp_1x1." in k:
																					UpperCamelCase__				     =			k_new.replace(""".exp_1x1."""   ,			""".expand_1x1."""       )
														if ".red_1x1." in k:
																					UpperCamelCase__				     =			k_new.replace(""".red_1x1."""   ,			""".reduce_1x1."""       )

														for i in [3, 4, 5]:
																					if f"""layer_{i}.0.""" in k:
																												UpperCamelCase__				     =			k_new.replace(f"""layer_{i}.0."""   ,			f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""       )
																					if f"""layer_{i}.1.local_rep.0.""" in k:
																												UpperCamelCase__				     =			k_new.replace(f"""layer_{i}.1.local_rep.0."""   ,			f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""       )
																					if f"""layer_{i}.1.local_rep.1.""" in k:
																												UpperCamelCase__				     =			k_new.replace(f"""layer_{i}.1.local_rep.1."""   ,			f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""       )

														for i in [3, 4, 5]:
																					if i == 3:
																												UpperCamelCase__				     =			[0, 1]
																					elif i == 4:
																												UpperCamelCase__				     =			[0, 1, 2, 3]
																					elif i == 5:
																												UpperCamelCase__				     =			[0, 1, 2]

																					for j in j_in:
																												if f"""layer_{i}.1.global_rep.{j}.""" in k:
																																			UpperCamelCase__				     =			k_new.replace(
																																			    f"""layer_{i}.1.global_rep.{j}."""   ,			f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""       )
																					if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
																												UpperCamelCase__				     =			k_new.replace(
																												    f"""layer_{i}.1.global_rep.{j+1}."""   ,			f"""{model_prefix}encoder.layer.{i-1}.layernorm."""       )

																					if f"""layer_{i}.1.conv_proj.""" in k:
																												UpperCamelCase__				     =			k_new.replace(f"""layer_{i}.1.conv_proj."""   ,			f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""       )

														if "pre_norm_attn.0." in k:
																					UpperCamelCase__				     =			k_new.replace("""pre_norm_attn.0."""   ,			"""layernorm_before."""       )
														if "pre_norm_attn.1." in k:
																					UpperCamelCase__				     =			k_new.replace("""pre_norm_attn.1."""   ,			"""attention."""       )
														if "pre_norm_ffn.0." in k:
																					UpperCamelCase__				     =			k_new.replace("""pre_norm_ffn.0."""   ,			"""layernorm_after."""       )
														if "pre_norm_ffn.1." in k:
																					UpperCamelCase__				     =			k_new.replace("""pre_norm_ffn.1."""   ,			"""ffn.conv1."""       )
														if "pre_norm_ffn.3." in k:
																					UpperCamelCase__				     =			k_new.replace("""pre_norm_ffn.3."""   ,			"""ffn.conv2."""       )

														if "classifier.1." in k:
																					UpperCamelCase__				     =			k_new.replace("""classifier.1."""   ,			"""classifier."""       )

														if "seg_head." in k:
																					UpperCamelCase__				     =			k_new.replace("""seg_head."""   ,			"""segmentation_head."""       )
														if ".aspp_layer." in k:
																					UpperCamelCase__				     =			k_new.replace(""".aspp_layer."""   ,			"""."""       )
														if ".aspp_pool." in k:
																					UpperCamelCase__				     =			k_new.replace(""".aspp_pool."""   ,			"""."""       )

														rename_keys.append((k, k_new)       )
							return rename_keys





def     _UpperCamelCase     (a__   :List[str]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							for k in state_dict.keys():
														if k.startswith("""seg_head.aux_head."""       ):
																					keys_to_ignore.append(a__       )
							for k in keys_to_ignore:
														state_dict.pop(a__   ,			a__       )





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							UpperCamelCase__				     =			Image.open(requests.get(a__   ,			stream=a__       ).raw       )
							return im





@torch.no_grad()
def     _UpperCamelCase     (a__   :Dict   ,			a__   :List[Any]   ,			a__   :Tuple   ,			a__   :List[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			get_mobilevitva_config(a__   ,			a__       )

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

							# load huggingface model
							if task_name.startswith("""ade20k_"""       ) or task_name.startswith("""voc_"""       ):
														UpperCamelCase__				     =			MobileViTVaForSemanticSegmentation(a__       ).eval()
														UpperCamelCase__				     =			False
							else:
														UpperCamelCase__				     =			MobileViTVaForImageClassification(a__       ).eval()
														UpperCamelCase__				     =			False

							# remove and rename some keys of load the original model
							UpperCamelCase__				     =			checkpoint
							remove_unused_keys(a__       )
							UpperCamelCase__				     =			create_rename_keys(a__   ,			base_model=a__       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(a__   ,			a__   ,			a__       )

							# load modified state_dict
							model.load_state_dict(a__       )

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

							# verify classification model
							if task_name.startswith("""imagenet"""       ):
														UpperCamelCase__				     =			outputs.logits
														UpperCamelCase__				     =			logits.argmax(-1       ).item()
														print("""Predicted class:"""   ,			model.config.idalabel[predicted_class_idx]       )
														if task_name.startswith("""imagenet1k_256"""       ) and config.width_multiplier == 1.0:
																					# expected_logits for base variant
																					UpperCamelCase__				     =			torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]       )
																					assert torch.allclose(logits[0, :3]   ,			a__   ,			atol=1e-4       )

							Path(a__       ).mkdir(exist_ok=a__       )
							print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""       )
							model.save_pretrained(a__       )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
							image_processor.save_pretrained(a__       )


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--task",
					    default="imagenet1k_256",
					    type=str,
					    help=(
					        "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
					        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
					    ),
					    choices=[
					        "imagenet1k_256",
					        "imagenet1k_384",
					        "imagenet21k_to_1k_256",
					        "imagenet21k_to_1k_384",
					        "ade20k_deeplabv3",
					        "voc_deeplabv3",
					    ],
					)

					parser.add_argument(
					    "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
					)
					parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
					parser.add_argument(
					    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
					)

					UpperCamelCase__											= parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)











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





def     _UpperCamelCase     (a__   :str   ,			a__   :complex   ,			a__   :str = "x"   ,			a__   :float = 10**-10   ,			a__   :int = 1   ,			):






							"""simple docstring"""





							UpperCamelCase__				     =			symbols(a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			diff(a__   ,			a__       )       )

							UpperCamelCase__				     =			starting_point

							while True:
														if diff_function(a__       ) != 0:
																					UpperCamelCase__				     =			prev_guess - multiplicity * func(a__       ) / diff_function(
																					    a__       )
														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

														UpperCamelCase__				     =			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=0.0_05)}""",
					)

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




