

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					SwinConfig(image_size=192   )

							if "base" in model_name:
														snake_case_           =					6
														snake_case_           =					128
														snake_case_           =					(2, 2, 18, 2)
														snake_case_           =					(4, 8, 16, 32)
							elif "large" in model_name:
														snake_case_           =					12
														snake_case_           =					192
														snake_case_           =					(2, 2, 18, 2)
														snake_case_           =					(6, 12, 24, 48)
							else:
														raise ValueError('Model not supported, only supports base and large variants'   )

							snake_case_           =					window_size
							snake_case_           =					embed_dim
							snake_case_           =					depths
							snake_case_           =					num_heads

							return config





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if "encoder.mask_token" in name:
														snake_case_           =					name.replace('encoder.mask_token'     ,							'embeddings.mask_token'   )
							if "encoder.patch_embed.proj" in name:
														snake_case_           =					name.replace('encoder.patch_embed.proj'     ,							'embeddings.patch_embeddings.projection'   )
							if "encoder.patch_embed.norm" in name:
														snake_case_           =					name.replace('encoder.patch_embed.norm'     ,							'embeddings.norm'   )
							if "attn.proj" in name:
														snake_case_           =					name.replace('attn.proj'     ,							'attention.output.dense'   )
							if "attn" in name:
														snake_case_           =					name.replace('attn'     ,							'attention.self'   )
							if "norm1" in name:
														snake_case_           =					name.replace('norm1'     ,							'layernorm_before'   )
							if "norm2" in name:
														snake_case_           =					name.replace('norm2'     ,							'layernorm_after'   )
							if "mlp.fc1" in name:
														snake_case_           =					name.replace('mlp.fc1'     ,							'intermediate.dense'   )
							if "mlp.fc2" in name:
														snake_case_           =					name.replace('mlp.fc2'     ,							'output.dense'   )

							if name == "encoder.norm.weight":
														snake_case_           =					'layernorm.weight'
							if name == "encoder.norm.bias":
														snake_case_           =					'layernorm.bias'

							if "decoder" in name:
														pass
							else:
														snake_case_           =					'swin.' + name

							return name





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

														if "attn_mask" in key:
																					pass
														elif "qkv" in key:
																					snake_case_           =					key.split('.'   )
																					snake_case_           =					int(key_split[2]   )
																					snake_case_           =					int(key_split[4]   )
																					snake_case_           =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												snake_case_           =					val[:dim, :]
																												snake_case_           =					val[
																												    dim : dim * 2, :
																												]
																												snake_case_           =					val[-dim:, :]
																					else:
																												snake_case_           =					val[
																												    :dim
																												]
																												snake_case_           =					val[
																												    dim : dim * 2
																												]
																												snake_case_           =					val[
																												    -dim:
																												]
														else:
																					snake_case_           =					val

							return orig_state_dict





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )['model']

							snake_case_           =					get_swin_config(UpperCamelCase__   )
							snake_case_           =					SwinForMaskedImageModeling(UpperCamelCase__   )
							model.eval()

							snake_case_           =					convert_state_dict(UpperCamelCase__     ,							UpperCamelCase__   )
							model.load_state_dict(UpperCamelCase__   )

							snake_case_           =					'http://images.cocodataset.org/val2017/000000039769.jpg'

							snake_case_           =					ViTImageProcessor(size={'height': 192, 'width': 192}   )
							snake_case_           =					Image.open(requests.get(UpperCamelCase__     ,							stream=UpperCamelCase__   ).raw   )
							snake_case_           =					image_processor(images=UpperCamelCase__     ,							return_tensors='pt'   )

							with torch.no_grad():
														snake_case_           =					model(**UpperCamelCase__   ).logits

							print(outputs.keys()   )
							print('Looks ok!'   )

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model {model_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:
														print(F'''Pushing model and image processor for {model_name} to hub'''   )
														model.push_to_hub(F'''microsoft/{model_name}'''   )
														image_processor.push_to_hub(F'''microsoft/{model_name}'''   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Tuple		       = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""swin-base-simmim-window6-192""",
					    type=str,
					    choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
					    help="""Name of the Swin SimMIM model you'd like to convert.""",
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
					    type=str,
					    help="""Path to the original PyTorch checkpoint (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, 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."""
					)

					_UpperCAmelCase	:     List[str]		       = parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







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




