
'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a       :      Optional[int]  	=			logging.get_logger(__name__)







@dataclass
class   UpperCamelCase__   :


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     str       =							42
		SCREAMING_SNAKE_CASE__  :     List[str]       =							field(default_factory=lowerCAmelCase_       )
		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							field(default_factory=lowerCAmelCase_       )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       len(list(m.modules()      )      ) == 1 or isinstance(_snake_case   ,      nn.Convad      ) or isinstance(_snake_case   ,      nn.BatchNormad      )
							if has_not_submodules:
												if not isinstance(_snake_case   ,      _snake_case      ):
																	self.traced.append(_snake_case      )







		def __call__(			self   ,      snake_case      ):


							'''simple docstring'''

							for m in self.module.modules():
												self.handles.append(m.register_forward_hook(self._forward_hook      )      )
							self.module(_snake_case      )
							[x.remove() for x in self.handles]
							return self







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return list(filter(lambda snake_case      : len(list(x.state_dict().keys()      )      ) > 0   ,      self.traced      )      )







@dataclass
class   UpperCamelCase__   :


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Any       =							42
		SCREAMING_SNAKE_CASE__  :     Dict       =							42
		SCREAMING_SNAKE_CASE__  :     int       =							0
		SCREAMING_SNAKE_CASE__  :     Union[str, Any]       =							field(default_factory=lowerCAmelCase_       )
		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							field(default_factory=lowerCAmelCase_       )







		def __call__(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       Tracker(self.dest      )(_snake_case      ).parametrized
							UpperCAmelCase		:							str        =       Tracker(self.src      )(_snake_case      ).parametrized

							UpperCAmelCase		:							Dict        =       list(filter(lambda snake_case      : type(_snake_case      ) not in self.src_skip   ,      _snake_case      )      )
							UpperCAmelCase		:							List[Any]        =       list(filter(lambda snake_case      : type(_snake_case      ) not in self.dest_skip   ,      _snake_case      )      )

							if len(_snake_case      ) != len(_snake_case      ):
												raise Exception(
												    f"Numbers of operations are different. Source module has {len(_snake_case      )} operations while"
												    f" destination module has {len(_snake_case      )}."      )

							for dest_m, src_m in zip(_snake_case   ,      _snake_case      ):
												dest_m.load_state_dict(src_m.state_dict()      )
												if self.verbose == 1:
																	print(f"Transfered from={src_m} to={dest_m}"      )





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[Any]        =       from_model.state_dict()
					UpperCAmelCase		:							int        =       our_model.state_dict()
					UpperCAmelCase		:							Union[str, Any]        =       our_model.config
					UpperCAmelCase		:							Dict        =       []
					for stage_idx in range(len(config.hidden_sizes							)							):
										for block_id in range(config.depths[stage_idx]							):
															UpperCAmelCase		:							Dict        =       F"block{stage_idx + 1}.{block_id}.layer_scale_1"
															UpperCAmelCase		:							Optional[Any]        =       F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

															all_keys.append((from_key, to_key)							)
															UpperCAmelCase		:							Dict        =       F"block{stage_idx + 1}.{block_id}.layer_scale_2"
															UpperCAmelCase		:							int        =       F"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

															all_keys.append((from_key, to_key)							)

					for from_key, to_key in all_keys:
										UpperCAmelCase		:							Union[str, Any]        =       from_state_dict.pop(__magic_name__							)

					our_model.load_state_dict(__magic_name__							)
					return our_model





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__ = True			,   ):



					'''simple docstring'''







					print(F"Downloading weights for {name}..."							)
					UpperCAmelCase		:							Dict        =       cached_download(__magic_name__							)
					print(F"Converting {name}..."							)
					UpperCAmelCase		:							Optional[Any]        =       torch.load(__magic_name__							)['state_dict']
					from_model.load_state_dict(__magic_name__							)
					from_model.eval()
					with torch.no_grad():
										UpperCAmelCase		:							Union[str, Any]        =       VanForImageClassification(__magic_name__							).eval()
										UpperCAmelCase		:							Tuple        =       ModuleTransfer(src=__magic_name__			,   dest=__magic_name__							)
										UpperCAmelCase		:							Optional[Any]        =       torch.randn((1, 3, 224, 224)							)
										module_transfer(__magic_name__							)
										UpperCAmelCase		:							int        =       copy_parameters(__magic_name__			,   __magic_name__							)

					if not torch.allclose(from_model(__magic_name__							)			,   our_model(__magic_name__							).logits							):
										raise ValueError("The model logits don\'t match the original one."							)

					UpperCAmelCase		:							int        =       name
					print(__magic_name__							)

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name			,   commit_message="Add model"			,   use_temp_dir=__magic_name__			,   )

										# we can use the convnext one
										UpperCAmelCase		:							Any        =       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"							)
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name			,   commit_message="Add image processor"			,   use_temp_dir=__magic_name__			,   )

										print(F"Pushed {checkpoint_name}"							)





def 				lowercase (					__magic_name__			,   __magic_name__ = None			,   __magic_name__ = True							):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       'imagenet-1k-id2label.json'
					UpperCAmelCase		:							List[str]        =       1000

					UpperCAmelCase		:							Dict        =       'huggingface/label-files'
					UpperCAmelCase		:							Optional[int]        =       num_labels
					UpperCAmelCase		:							Optional[Any]        =       json.load(open(hf_hub_download(__magic_name__			,   __magic_name__			,   repo_type="dataset"							)			,   "r"							)							)
					UpperCAmelCase		:							Dict        =       {int(__magic_name__							): v for k, v in idalabel.items()}

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

					UpperCAmelCase		:							Union[str, Any]        =       partial(__magic_name__			,   num_labels=__magic_name__			,   idalabel=__magic_name__			,   labelaid=__magic_name__							)

					UpperCAmelCase		:							Union[str, Any]        =       {
					    'van-tiny': ImageNetPreTrainedConfig(
					        hidden_sizes=[32, 64, 160, 256]			,   depths=[3, 3, 5, 2]			,   mlp_ratios=[8, 8, 4, 4]			,   ),
					    'van-small': ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]			,   depths=[2, 2, 4, 2]			,   mlp_ratios=[8, 8, 4, 4]			,   ),
					    'van-base': ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]			,   depths=[3, 3, 12, 3]			,   mlp_ratios=[8, 8, 4, 4]			,   ),
					    'van-large': ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]			,   depths=[3, 5, 27, 3]			,   mlp_ratios=[8, 8, 4, 4]			,   ),
					}

					UpperCAmelCase		:							Tuple        =       {
					    'van-tiny': van_tiny,
					    'van-small': van_small,
					    'van-base': van_base,
					    'van-large': van_large,
					}

					UpperCAmelCase		:							int        =       {
					    'van-tiny': (
					        'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
					    ),
					    'van-small': (
					        'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
					    ),
					    'van-base': (
					        'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
					    ),
					    'van-large': (
					        'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
					    ),
					}

					if model_name:
										convert_weight_and_push(
										    __magic_name__			,   names_to_config[model_name]			,   checkpoint=names_to_original_checkpoints[model_name]			,   from_model=names_to_original_models[model_name]()			,   save_directory=__magic_name__			,   push_to_hub=__magic_name__			,   )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    __magic_name__			,   __magic_name__			,   checkpoint=names_to_original_checkpoints[model_name]			,   from_model=names_to_original_models[model_name]()			,   save_directory=__magic_name__			,   push_to_hub=__magic_name__			,   )


if __name__ == "__main__":
				a       :      int  	=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model-name",
				    default=None,
				    type=str,
				    help=(
				        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
				        " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=Path,
				    required=True,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument(
				    "--van_dir",
				    required=True,
				    type=Path,
				    help=(
				        "A path to VAN's original implementation directory. You can download from here:"
				        " https://github.com/Visual-Attention-Network/VAN-Classification"
				    ),
				)
				parser.add_argument(
				    "--push_to_hub",
				    default=True,
				    type=bool,
				    required=False,
				    help="If True, push model and image processor to the hub.",
				)

				a       :      Dict  	=			parser.parse_args()
				a       :      Path  	=			args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				a       :      Optional[int]  	=			args.van_dir
				# append the path to the parents to maskformer dir
				sys.path.append(str(van_dir.parent))
				from van.models.van import van_base, van_large, van_small, van_tiny

				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(
							    snake_case   ,      split=snake_case   ,      features=snake_case   ,      cache_dir=snake_case   ,      keep_in_memory=snake_case   ,      streaming=snake_case   ,      num_proc=snake_case   ,      **snake_case   ,      )
							UpperCAmelCase		:							str        =       path_or_paths if isinstance(snake_case   ,      snake_case      ) else {self.split: path_or_paths}
							UpperCAmelCase		:							int        =       Csv(
							    cache_dir=snake_case   ,      data_files=snake_case   ,      features=snake_case   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							if self.streaming:
												UpperCAmelCase		:							Optional[Any]        =       self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							int        =       None

												self.builder.download_and_prepare(
												    download_config=snake_case   ,      download_mode=snake_case   ,      verification_mode=snake_case   ,      base_path=snake_case   ,      num_proc=self.num_proc   ,      )
												UpperCAmelCase		:							Tuple        =       self.builder.as_dataset(
												    split=self.split   ,      verification_mode=snake_case   ,      in_memory=self.keep_in_memory      )
							return dataset







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."      )

							UpperCAmelCase		:							Tuple        =       dataset
							UpperCAmelCase		:							Dict        =       path_or_buf
							UpperCAmelCase		:							List[Any]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							str        =       num_proc
							UpperCAmelCase		:							List[Any]        =       "utf-8"
							UpperCAmelCase		:							Tuple        =       to_csv_kwargs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.to_csv_kwargs.pop("path_or_buf"   ,      snake_case      )
							UpperCAmelCase		:							Any        =       self.to_csv_kwargs.pop("header"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       self.to_csv_kwargs.pop("index"   ,      snake_case      )

							if isinstance(self.path_or_buf   ,      (str, bytes, os.PathLike)      ):
												with open(self.path_or_buf   ,      "wb+"      ) as buffer:
																	UpperCAmelCase		:							Tuple        =       self._write(file_obj=snake_case   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							else:
												UpperCAmelCase		:							Any        =       self._write(file_obj=self.path_or_buf   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							return written







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							Optional[int]        =       args

							UpperCAmelCase		:							List[str]        =       query_table(
							    table=self.dataset.data   ,      key=slice(snake_case   ,      offset + self.batch_size      )   ,      indices=self.dataset._indices   ,      )
							UpperCAmelCase		:							int        =       batch.to_pandas().to_csv(
							    path_or_buf=snake_case   ,      header=header if (offset == 0) else False   ,      index=snake_case   ,      **snake_case      )
							return csv_str.encode(self.encoding      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0   ,      len(self.dataset      )   ,      self.batch_size      )   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																	UpperCAmelCase		:							Any        =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																	written += file_obj.write(snake_case      )

							else:
												UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       len(self.dataset      ), self.batch_size
												with multiprocessing.Pool(self.num_proc      ) as pool:
																	for csv_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_csv   ,      [(offset, header, index, to_csv_kwargs) for offset in range(0   ,      snake_case   ,      snake_case      )]   ,      )   ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																						written += file_obj.write(snake_case      )

							return written
