

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: List[str]    ,      lowercase_: Tuple    ,      lowercase_: List[str]=1024					)     ->      Union[str, Any]:
	A__   ,   A__							:    Dict        =		[], []

	A__							:    List[str]        =		list(zip(lowercase_    ,      lowercase_					)					)
	A__   ,   A__							:    Any        =		sorted_examples[0]

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

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

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

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

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


if __name__ == "__main__":
	packer_cli()






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



