



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    SwinConfig(image_size=192  )

							if "base" in model_name:
														_lowerCAmelCase     :							List[Any]    =    6
														_lowerCAmelCase     :							Tuple    =    128
														_lowerCAmelCase     :							Union[str, Any]    =    (2, 2, 18, 2)
														_lowerCAmelCase     :							Optional[int]    =    (4, 8, 16, 32)
							elif "large" in model_name:
														_lowerCAmelCase     :							Optional[int]    =    12
														_lowerCAmelCase     :							Union[str, Any]    =    192
														_lowerCAmelCase     :							Dict    =    (2, 2, 18, 2)
														_lowerCAmelCase     :							List[Any]    =    (6, 12, 24, 48)
							else:
														raise ValueError("Model not supported, only supports base and large variants"  )

							_lowerCAmelCase     :							Optional[int]    =    window_size
							_lowerCAmelCase     :							str    =    embed_dim
							_lowerCAmelCase     :							Union[str, Any]    =    depths
							_lowerCAmelCase     :							Tuple    =    num_heads

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "encoder.mask_token" in name:
														_lowerCAmelCase     :							str    =    name.replace("encoder.mask_token"				, "embeddings.mask_token"  )
							if "encoder.patch_embed.proj" in name:
														_lowerCAmelCase     :							Tuple    =    name.replace("encoder.patch_embed.proj"				, "embeddings.patch_embeddings.projection"  )
							if "encoder.patch_embed.norm" in name:
														_lowerCAmelCase     :							Optional[int]    =    name.replace("encoder.patch_embed.norm"				, "embeddings.norm"  )
							if "attn.proj" in name:
														_lowerCAmelCase     :							Tuple    =    name.replace("attn.proj"				, "attention.output.dense"  )
							if "attn" in name:
														_lowerCAmelCase     :							Tuple    =    name.replace("attn"				, "attention.self"  )
							if "norm1" in name:
														_lowerCAmelCase     :							str    =    name.replace("norm1"				, "layernorm_before"  )
							if "norm2" in name:
														_lowerCAmelCase     :							Dict    =    name.replace("norm2"				, "layernorm_after"  )
							if "mlp.fc1" in name:
														_lowerCAmelCase     :							Union[str, Any]    =    name.replace("mlp.fc1"				, "intermediate.dense"  )
							if "mlp.fc2" in name:
														_lowerCAmelCase     :							Tuple    =    name.replace("mlp.fc2"				, "output.dense"  )

							if name == "encoder.norm.weight":
														_lowerCAmelCase     :							str    =    "layernorm.weight"
							if name == "encoder.norm.bias":
														_lowerCAmelCase     :							Dict    =    "layernorm.bias"

							if "decoder" in name:
														pass
							else:
														_lowerCAmelCase     :							int    =    "swin." + name

							return name






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase     :							Optional[Any]    =    orig_state_dict.pop(_lowerCamelCase  )

														if "attn_mask" in key:
																					pass
														elif "qkv" in key:
																					_lowerCAmelCase     :							Union[str, Any]    =    key.split("."  )
																					_lowerCAmelCase     :							Tuple    =    int(key_split[2]  )
																					_lowerCAmelCase     :							Union[str, Any]    =    int(key_split[4]  )
																					_lowerCAmelCase     :							Optional[int]    =    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												_lowerCAmelCase     :							Dict    =    val[:dim, :]
																												_lowerCAmelCase     :							List[Any]    =    val[
																												    dim : dim * 2, :
																												]
																												_lowerCAmelCase     :							Optional[Any]    =    val[-dim:, :]
																					else:
																												_lowerCAmelCase     :							List[Any]    =    val[
																												    :dim
																												]
																												_lowerCAmelCase     :							List[str]    =    val[
																												    dim : dim * 2
																												]
																												_lowerCAmelCase     :							int    =    val[
																												    -dim:
																												]
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    val

							return orig_state_dict






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    torch.load(_lowerCamelCase				, map_location="cpu"  )["model"]

							_lowerCAmelCase     :							Optional[int]    =    get_swin_config(_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    SwinForMaskedImageModeling(_lowerCamelCase  )
							model.eval()

							_lowerCAmelCase     :							Tuple    =    convert_state_dict(_lowerCamelCase				, _lowerCamelCase  )
							model.load_state_dict(_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    "http://images.cocodataset.org/val2017/000000039769.jpg"

							_lowerCAmelCase     :							Union[str, Any]    =    ViTImageProcessor(size={"height": 192, "width": 192}  )
							_lowerCAmelCase     :							Dict    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							_lowerCAmelCase     :							Dict    =    image_processor(images=_lowerCamelCase				, return_tensors="pt"  )

							with torch.no_grad():
														_lowerCAmelCase     :							Any    =    model(**_lowerCamelCase  ).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(_lowerCamelCase  )

														print(F"Saving image processor to {pytorch_dump_folder_path}"  )
														image_processor.save_pretrained(_lowerCamelCase  )

							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__":
						_snake_case		=  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."
						)

						_snake_case		=  parser.parse_args()
						convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case		=  get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		BartphoTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							str    =    ["▁This", "▁is", "▁a", "▁t", "est"]
														_lowerCAmelCase     :							List[str]    =    dict(zip(__a,			range(len(__a))))
														_lowerCAmelCase     :							Optional[Any]    =    {"unk_token": "<unk>"}

														_lowerCAmelCase     :							Optional[int]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["monolingual_vocab_file"])
														with open(self.monolingual_vocab_file,			"w",			encoding="utf-8") as fp:
																					for token in vocab_tokens:
																												fp.write(f"{token} {vocab_tokens[token]}\n")

														_lowerCAmelCase     :							Optional[Any]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return BartphoTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "This is a là test"
														_lowerCAmelCase     :							Optional[int]    =    "This is a<unk><unk> test"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														_lowerCAmelCase     :							List[Any]    =    "This is a là test"
														_lowerCAmelCase     :							str    =    "▁This ▁is ▁a ▁l à ▁t est".split()
														_lowerCAmelCase     :							str    =    tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Tuple    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							List[str]    =    [4, 5, 6, 3, 3, 7, 8, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)
