





'''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 ) ->				List[str]:



			"""simple docstring"""


			__snake_case      ,     __snake_case					:      int							      =     [], []

			__snake_case					:      List[str]							      =     list(zip(_lowerCamelCase    ,	_lowerCamelCase ) )
			__snake_case      ,     __snake_case					:      Union[str, Any]							      =     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:] ):
						__snake_case					:      Union[str, Any]							      =     new_src + """ """ + src
						__snake_case					:      List[Any]							      =     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 )
									__snake_case      ,     __snake_case					:      Optional[int]							      =     src, tgt
						else:  # can fit, keep adding
									__snake_case      ,     __snake_case					:      Dict							      =     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 ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     Path(_lowerCamelCase )
			save_path.mkdir(exist_ok=_lowerCamelCase )
			for split in ["train"]:
						__snake_case      ,     __snake_case					:      int							      =     data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
						__snake_case					:      Union[str, Any]							      =     [x.rstrip() for x in Path(_lowerCamelCase ).open().readlines()]
						__snake_case					:      str							      =     [x.rstrip() for x in Path(_lowerCamelCase ).open().readlines()]
						__snake_case      ,     __snake_case					:      Optional[Any]							      =     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"]:
						__snake_case      ,     __snake_case					:      Any							      =     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						(       ) ->				int:



			"""simple docstring"""


			__snake_case					:      List[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 )
			__snake_case					:      int							      =     parser.parse_args()
			__snake_case					:      List[str]							      =     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()









'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

__UpperCamelCase					    =	[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(_lowerCamelCase )
			m.model.load_state_dict(_lowerCamelCase    ,	strict=_lowerCamelCase )
			m.half()
			m.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
	parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
	parser.add_argument(
	    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


