





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if "model" in orig_key:
														snake_case_           =					orig_key.replace('model.'     ,							''   )
							if "norm1" in orig_key:
														snake_case_           =					orig_key.replace('norm1'     ,							'attention.output.LayerNorm'   )
							if "norm2" in orig_key:
														snake_case_           =					orig_key.replace('norm2'     ,							'output.LayerNorm'   )
							if "norm" in orig_key:
														snake_case_           =					orig_key.replace('norm'     ,							'LayerNorm'   )
							if "transformer" in orig_key:
														snake_case_           =					orig_key.split('.'   )[0].split('_'   )[-1]
														snake_case_           =					orig_key.replace(F'''transformer_{layer_num}'''     ,							F'''encoder.layer.{layer_num}'''   )
							if "mha.attn" in orig_key:
														snake_case_           =					orig_key.replace('mha.attn'     ,							'attention.self'   )
							if "mha" in orig_key:
														snake_case_           =					orig_key.replace('mha'     ,							'attention'   )
							if "W_q" in orig_key:
														snake_case_           =					orig_key.replace('W_q'     ,							'self.query'   )
							if "W_k" in orig_key:
														snake_case_           =					orig_key.replace('W_k'     ,							'self.key'   )
							if "W_v" in orig_key:
														snake_case_           =					orig_key.replace('W_v'     ,							'self.value'   )
							if "ff1" in orig_key:
														snake_case_           =					orig_key.replace('ff1'     ,							'intermediate.dense'   )
							if "ff2" in orig_key:
														snake_case_           =					orig_key.replace('ff2'     ,							'output.dense'   )
							if "ff" in orig_key:
														snake_case_           =					orig_key.replace('ff'     ,							'output.dense'   )
							if "mlm_class" in orig_key:
														snake_case_           =					orig_key.replace('mlm.mlm_class'     ,							'cls.predictions.decoder'   )
							if "mlm" in orig_key:
														snake_case_           =					orig_key.replace('mlm'     ,							'cls.predictions.transform'   )
							if "cls" not in orig_key:
														snake_case_           =					'yoso.' + orig_key

							return orig_key





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														snake_case_           =					orig_state_dict.pop(UpperCamelCase__   )

														if ("pooler" in key) or ("sen_class" in key):
																					continue
														else:
																					snake_case_           =					val

							snake_case_           =					orig_state_dict['cls.predictions.decoder.bias']
							snake_case_           =					torch.arange(UpperCamelCase__   ).expand((1, -1)   ) + 2

							return orig_state_dict





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )['model_state_dict']
							snake_case_           =					YosoConfig.from_json_file(UpperCamelCase__   )
							snake_case_           =					YosoForMaskedLM(UpperCamelCase__   )

							snake_case_           =					convert_checkpoint_helper(config.max_position_embeddings     ,							UpperCamelCase__   )

							print(model.load_state_dict(UpperCamelCase__   )   )
							model.eval()
							model.save_pretrained(UpperCamelCase__   )

							print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Dict		       = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The json file for YOSO model config.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_UpperCAmelCase	:     str		       = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

							def set_batch_size(UpperCamelCase__   ) -> None:
														nonlocal batch_size
														if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and feature.dtype == "binary":
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

							_visit(UpperCamelCase__     ,							UpperCamelCase__   )

							return None if batch_size is np.inf else batch_size







class 				lowercase       ( lowercase_    ):




				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   ,     ):
											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   ,     )
											snake_case_           =					path_or_paths if isinstance(snake_case   ,     snake_case ) else {self.split: path_or_paths}
											snake_case_           =					_PACKAGED_DATASETS_MODULES['parquet'][1]
											snake_case_           =					Parquet(
											    cache_dir=snake_case   ,     data_files=snake_case   ,     features=snake_case   ,     hash=snake_case   ,     **snake_case   ,     )




				def 					a							(      self ):
											# Build iterable dataset
											if self.streaming:
																		snake_case_           =					self.builder.as_streaming_dataset(split=self.split )
											# Build regular (map-style) dataset
											else:
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					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   ,     )
																		snake_case_           =					self.builder.as_dataset(
																		    split=self.split   ,     verification_mode=snake_case   ,     in_memory=self.keep_in_memory )
											return dataset







class 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					dataset
											snake_case_           =					path_or_buf
											snake_case_           =					batch_size or get_writer_batch_size(dataset.features )
											snake_case_           =					parquet_writer_kwargs




				def 					a							(      self ):
											snake_case_           =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf   ,     (str, bytes, os.PathLike) ):
																		with open(self.path_or_buf   ,     'wb+' ) as buffer:
																									snake_case_           =					self._write(file_obj=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

											for offset in logging.tqdm(
											    range(0   ,     len(self.dataset )   ,     snake_case )   ,     unit='ba'   ,     disable=not logging.is_progress_bar_enabled()   ,     desc='Creating parquet from Arrow format'   ,     ):
																		snake_case_           =					query_table(
																		    table=self.dataset._data   ,     key=slice(snake_case   ,     offset + batch_size )   ,     indices=self.dataset._indices if self.dataset._indices is not None else None   ,     )
																		writer.write_table(snake_case )
																		written += batch.nbytes
											writer.close()
											return written




