"""simple docstring"""



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase							):
							A__       :     Dict								=			SwinConfig.from_pretrained(
							    """microsoft/swin-tiny-patch4-window7-224"""			,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							)
							A__       :     str								=			MaskFormerConfig(backbone_config=lowerCAmelCase							)

							A__       :     Optional[int]								=			"""huggingface/label-files"""
							if "ade20k-full" in model_name:
														# this should be ok
														A__       :     str								=			847
														A__       :     Any								=			"""maskformer-ade20k-full-id2label.json"""
							elif "ade" in model_name:
														# this should be ok
														A__       :     List[str]								=			150
														A__       :     List[Any]								=			"""ade20k-id2label.json"""
							elif "coco-stuff" in model_name:
														# this should be ok
														A__       :     Dict								=			171
														A__       :     int								=			"""maskformer-coco-stuff-id2label.json"""
							elif "coco" in model_name:
														# TODO
														A__       :     Union[str, Any]								=			133
														A__       :     Any								=			"""coco-panoptic-id2label.json"""
							elif "cityscapes" in model_name:
														# this should be ok
														A__       :     int								=			19
														A__       :     str								=			"""cityscapes-id2label.json"""
							elif "vistas" in model_name:
														# this should be ok
														A__       :     Union[str, Any]								=			65
														A__       :     Optional[Any]								=			"""mapillary-vistas-id2label.json"""

							A__       :     int								=			json.load(open(hf_hub_download(lowerCAmelCase			,  lowerCAmelCase			,  repo_type="""dataset"""							)			,  """r"""							)							)
							A__       :     str								=			{int(lowerCAmelCase							): v for k, v in idalabel.items()}

							return config

def  _A(		lowerCAmelCase							):
							A__       :     Union[str, Any]								=			[]
							# stem
							# fmt: off
							rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")							)
							rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")							)
							rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")							)
							rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")							)
							# stages
							for i in range(len(config.backbone_config.depths							)							):
														for j in range(config.backbone_config.depths[i]							):
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')							)

														if i < 3:
																					rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')							)
																					rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')							)
														rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')							)
														rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')							)

							# FPN
							rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")							)
							rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")							)
							rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")							)
							for source_index, target_index in zip(range(3			,  0			,  -1							)			,  range(0			,  3							)							):
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')							)
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')							)
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')							)
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')							)
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')							)
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')							)
							rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")							)
							rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")							)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers							):
														# self-attention out projection
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')							)
														# cross-attention out projection
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')							)
														# MLP 1
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')							)
														# MLP 2
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')							)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')							)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')							)
														# layernorm 3 (final layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')							)

							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")							)
							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")							)

							# heads on top
							rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")							)

							rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")							)
							rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")							)

							rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")							)
							rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")							)

							for i in range(3							):
														rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')							)
														rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')							)
							# fmt: on

							return rename_keys

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			dct.pop(lowerCAmelCase							)
							A__       :     Tuple								=			val

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Tuple								=			[int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
							for i in range(len(backbone_config.depths							)							):
														A__       :     List[Any]								=			num_features[i]
														for j in range(backbone_config.depths[i]							):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					A__       :     int								=			state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''							)
																					A__       :     Any								=			state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''							)
																					# next, add query, keys and values (in that order) to the state dict
																					A__       :     List[str]								=			in_proj_weight[:dim, :]
																					A__       :     Any								=			in_proj_bias[: dim]
																					A__       :     Optional[Any]								=			in_proj_weight[
																					    dim : dim * 2, :
																					]
																					A__       :     List[str]								=			in_proj_bias[
																					    dim : dim * 2
																					]
																					A__       :     Any								=			in_proj_weight[
																					    -dim :, :
																					]
																					A__       :     str								=			in_proj_bias[-dim :]
																					# fmt: on

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# fmt: off
							A__       :     str								=			config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers							):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														A__       :     Any								=			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''							)
														A__       :     Union[str, Any]								=			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''							)
														# next, add query, keys and values (in that order) to the state dict
														A__       :     str								=			in_proj_weight[: hidden_size, :]
														A__       :     Dict								=			in_proj_bias[:config.hidden_size]
														A__       :     List[str]								=			in_proj_weight[hidden_size : hidden_size * 2, :]
														A__       :     List[Any]								=			in_proj_bias[hidden_size : hidden_size * 2]
														A__       :     int								=			in_proj_weight[-hidden_size :, :]
														A__       :     Optional[Any]								=			in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														A__       :     int								=			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''							)
														A__       :     Any								=			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''							)
														# next, add query, keys and values (in that order) to the state dict
														A__       :     List[Any]								=			in_proj_weight[: hidden_size, :]
														A__       :     int								=			in_proj_bias[:config.hidden_size]
														A__       :     List[str]								=			in_proj_weight[hidden_size : hidden_size * 2, :]
														A__       :     Union[str, Any]								=			in_proj_bias[hidden_size : hidden_size * 2]
														A__       :     str								=			in_proj_weight[-hidden_size :, :]
														A__       :     Union[str, Any]								=			in_proj_bias[-hidden_size :]
							# fmt: on

def  _A(		):
							A__       :     Optional[int]								=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							A__       :     Any								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							)
							return im

@torch.no_grad()
def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase = False							):
							A__       :     str								=			get_maskformer_config(lowerCAmelCase							)

							# load original state_dict
							with open(lowerCAmelCase			,  """rb"""							) as f:
														A__       :     List[str]								=			pickle.load(lowerCAmelCase							)
							A__       :     int								=			data["""model"""]

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							A__       :     Optional[Any]								=			create_rename_keys(lowerCAmelCase							)
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							read_in_swin_q_k_v(lowerCAmelCase			,  config.backbone_config							)
							read_in_decoder_q_k_v(lowerCAmelCase			,  lowerCAmelCase							)

							# update to torch tensors
							for key, value in state_dict.items():
														A__       :     List[str]								=			torch.from_numpy(lowerCAmelCase							)

							# load 🤗 model
							A__       :     Union[str, Any]								=			MaskFormerForInstanceSegmentation(lowerCAmelCase							)
							model.eval()

							for name, param in model.named_parameters():
														print(lowerCAmelCase			,  param.shape							)

							A__				,							A__       :     Tuple								=			model.load_state_dict(lowerCAmelCase			,  strict=lowerCAmelCase							)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(lowerCAmelCase							) == 0, F'''Unexpected keys: {unexpected_keys}'''

							# verify results
							A__       :     Tuple								=			prepare_img()
							if "vistas" in model_name:
														A__       :     List[str]								=			65
							elif "cityscapes" in model_name:
														A__       :     List[Any]								=			6_5535
							else:
														A__       :     Dict								=			255
							A__       :     int								=			True if """ade""" in model_name else False
							A__       :     Optional[int]								=			MaskFormerImageProcessor(ignore_index=lowerCAmelCase			,  reduce_labels=lowerCAmelCase							)

							A__       :     Optional[int]								=			image_processor(lowerCAmelCase			,  return_tensors="""pt"""							)

							A__       :     Optional[int]								=			model(**lowerCAmelCase							)

							print("""Logits:"""			,  outputs.class_queries_logits[0, :3, :3]							)

							if model_name == "maskformer-swin-tiny-ade":
														A__       :     Any								=			torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]							)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,  lowerCAmelCase			,  atol=1E-4							)
							print("""Looks ok!"""							)

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''							)
														Path(lowerCAmelCase							).mkdir(exist_ok=lowerCAmelCase							)
														model.save_pretrained(lowerCAmelCase							)
														image_processor.save_pretrained(lowerCAmelCase							)

							if push_to_hub:
														print("""Pushing model and image processor to the hub..."""							)
														model.push_to_hub(F'''nielsr/{model_name}'''							)
														image_processor.push_to_hub(F'''nielsr/{model_name}'''							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="maskformer-swin-tiny-ade",
				    type=str,
				    help=("Name of the MaskFormer model you'd like to convert",),
				)
				parser.add_argument(
				    "--checkpoint_path",
				    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
				    type=str,
				    help="Path to the original state dict (.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								=		parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





