






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_       			=   logging.get_logger(__name__)


if is_vision_available():
			import PIL







class     UpperCamelCase_ (_lowercase  ):
	__magic_name__			   =       ['''pixel_values''']





	def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Dict[str, int] = None			,			lowerCAmelCase_      :				PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Dict[str, int] = None			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Union[int, float] = 1 / 255			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				bool = True			,			**lowerCAmelCase_      :				Optional[Any]			,			)		->    None:
				super().__init__(**A_		)
				UpperCAmelCase_  :				List[str]      =      size if size is not None else {"shortest_edge": 224}
				UpperCAmelCase_  :				List[str]      =      get_size_dict(A_			,			default_to_square=A_		)
				UpperCAmelCase_  :				List[Any]      =      crop_size if crop_size is not None else {"height": 224, "width": 224}
				UpperCAmelCase_  :				List[str]      =      get_size_dict(A_			,			default_to_square=A_			,			param_name="crop_size"		)

				UpperCAmelCase_  :				List[Any]      =      do_resize
				UpperCAmelCase_  :				List[Any]      =      size
				UpperCAmelCase_  :				Dict      =      resample
				UpperCAmelCase_  :				Optional[int]      =      do_center_crop
				UpperCAmelCase_  :				Dict      =      crop_size
				UpperCAmelCase_  :				Union[str, Any]      =      do_rescale
				UpperCAmelCase_  :				Optional[Any]      =      rescale_factor
				UpperCAmelCase_  :				int      =      do_normalize
				UpperCAmelCase_  :				str      =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				UpperCAmelCase_  :				Optional[int]      =      image_std if image_std is not None else OPENAI_CLIP_STD
				UpperCAmelCase_  :				Optional[int]      =      do_convert_rgb





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Dict[str, int]			,			lowerCAmelCase_      :				PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				Union[str, Any]			,			)		->    np.ndarray:
				UpperCAmelCase_  :				int      =      get_size_dict(A_			,			default_to_square=A_		)
				if "shortest_edge" not in size:
							raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
				UpperCAmelCase_  :				Tuple      =      get_resize_output_image_size(A_			,			size=size["shortest_edge"]			,			default_to_square=A_		)
				return resize(A_			,			size=A_			,			resample=A_			,			data_format=A_			,			**A_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Dict[str, int]			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				str			,			)		->    np.ndarray:
				UpperCAmelCase_  :				Any      =      get_size_dict(A_		)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
				return center_crop(A_			,			size=(size["height"], size["width"])			,			data_format=A_			,			**A_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Union[int, float]			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				Union[str, Any]			,			)		->    Union[str, Any]:
				return rescale(A_			,			scale=A_			,			data_format=A_			,			**A_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Union[float, List[float]]			,			lowerCAmelCase_      :				Union[float, List[float]]			,			lowerCAmelCase_      :				Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_      :				List[Any]			,			)		->    np.ndarray:
				return normalize(A_			,			mean=A_			,			std=A_			,			data_format=A_			,			**A_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				ImageInput			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				Dict[str, int] = None			,			lowerCAmelCase_      :				PILImageResampling = None			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				int = None			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				float = None			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_      :				bool = None			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			lowerCAmelCase_      :				Optional[ChannelDimension] = ChannelDimension.FIRST			,			**lowerCAmelCase_      :				Dict			,			)		->    PIL.Image.Image:
				UpperCAmelCase_  :				str      =      do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_  :				Dict      =      size if size is not None else self.size
				UpperCAmelCase_  :				int      =      get_size_dict(A_			,			param_name="size"			,			default_to_square=A_		)
				UpperCAmelCase_  :				List[str]      =      resample if resample is not None else self.resample
				UpperCAmelCase_  :				Any      =      do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase_  :				Optional[int]      =      crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase_  :				str      =      get_size_dict(A_			,			param_name="crop_size"			,			default_to_square=A_		)
				UpperCAmelCase_  :				str      =      do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_  :				Union[str, Any]      =      rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_  :				Optional[int]      =      do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase_  :				Union[str, Any]      =      image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase_  :				Union[str, Any]      =      image_std if image_std is not None else self.image_std
				UpperCAmelCase_  :				List[Any]      =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				UpperCAmelCase_  :				Tuple      =      make_list_of_images(A_		)

				if not valid_images(A_		):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."		)

				if do_resize and size is None:
							raise ValueError("Size must be specified if do_resize is True."		)

				if do_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True."		)

				if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True."		)

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and std must be specified if do_normalize is True."		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
							UpperCAmelCase_  :				Tuple      =      [convert_to_rgb(A_		) for image in images]

				# All transformations expect numpy arrays.
				UpperCAmelCase_  :				int      =      [to_numpy_array(A_		) for image in images]

				if do_resize:
							UpperCAmelCase_  :				Any      =      [self.resize(image=A_			,			size=A_			,			resample=A_		) for image in images]

				if do_center_crop:
							UpperCAmelCase_  :				List[str]      =      [self.center_crop(image=A_			,			size=A_		) for image in images]

				if do_rescale:
							UpperCAmelCase_  :				Optional[int]      =      [self.rescale(image=A_			,			scale=A_		) for image in images]

				if do_normalize:
							UpperCAmelCase_  :				int      =      [self.normalize(image=A_			,			mean=A_			,			std=A_		) for image in images]

				UpperCAmelCase_  :				List[Any]      =      [to_channel_dimension_format(A_			,			A_		) for image in images]

				UpperCAmelCase_  :				List[str]      =      {"pixel_values": images}
				return BatchFeature(data=A_			,			tensor_type=A_		)











"""simple docstring"""
import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Tuple      =      []
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
			        F"""stage{idx}.patch_embed.proj.weight""",
			    )		)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
			        F"""stage{idx}.patch_embed.proj.bias""",
			    )		)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
			        F"""stage{idx}.patch_embed.norm.weight""",
			    )		)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
			        F"""stage{idx}.patch_embed.norm.bias""",
			    )		)
			return embed

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      []
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
			    )		)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
			    )		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""")		)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""")		)
			return attention_weights

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")		)
			return token

def        snake_case   (     ):
			UpperCAmelCase_  :				str      =      []
			head.append(("layernorm.weight", "norm.weight")		)
			head.append(("layernorm.bias", "norm.bias")		)
			head.append(("classifier.weight", "head.weight")		)
			head.append(("classifier.bias", "head.bias")		)
			return head

def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      10_00

			UpperCAmelCase_  :				Any      =      "huggingface/label-files"
			UpperCAmelCase_  :				Optional[int]      =      num_labels
			UpperCAmelCase_  :				Union[str, Any]      =      json.load(open(cached_download(hf_hub_url(A__			,A__			,repo_type="dataset"		)		)			,"r"		)		)
			UpperCAmelCase_  :				Optional[int]      =      {int(A__		): v for k, v in idalabel.items()}

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

			UpperCAmelCase_  :				str      =      CvtConfig(num_labels=A__			,idalabel=A__			,labelaid=A__		)

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit("/"			,1		)[-1][4:6] == "13":
						UpperCAmelCase_  :				Optional[int]      =      [1, 2, 10]

			# For depth size 21 (21 = 1+4+16)
			elif cvt_model.rsplit("/"			,1		)[-1][4:6] == "21":
						UpperCAmelCase_  :				str      =      [1, 4, 16]

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						UpperCAmelCase_  :				Optional[int]      =      [2, 2, 20]
						UpperCAmelCase_  :				List[str]      =      [3, 12, 16]
						UpperCAmelCase_  :				Optional[Any]      =      [1_92, 7_68, 10_24]

			UpperCAmelCase_  :				Tuple      =      CvtForImageClassification(A__		)
			UpperCAmelCase_  :				Tuple      =      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
			UpperCAmelCase_  :				int      =      image_size
			UpperCAmelCase_  :				List[str]      =      torch.load(A__			,map_location=torch.device("cpu"		)		)

			UpperCAmelCase_  :				int      =      OrderedDict()
			UpperCAmelCase_  :				Optional[Any]      =      []

			for idx in range(len(config.depth		)		):
						if config.cls_token[idx]:
									UpperCAmelCase_  :				Optional[Any]      =      list_of_state_dict + cls_token(A__		)
						UpperCAmelCase_  :				Any      =      list_of_state_dict + embeddings(A__		)
						for cnt in range(config.depth[idx]		):
									UpperCAmelCase_  :				Dict      =      list_of_state_dict + attention(A__			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__		)
			for i in range(len(A__		)		):
						UpperCAmelCase_  :				Any      =      original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(A__		)
			model.save_pretrained(A__		)
			image_processor.save_pretrained(A__		)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
			lowerCamelCase_       			=   argparse.ArgumentParser()
			parser.add_argument(
			    '''--cvt_model''',
			    default='''cvt-w24''',
			    type=str,
			    help='''Name of the cvt model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=384,
			    type=int,
			    help='''Input Image Size''',
			)
			parser.add_argument(
			    '''--cvt_file_name''',
			    default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
			    type=str,
			    help='''Input Image Size''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			lowerCamelCase_       			=   parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



