

import os
from collections.abc import Iterator






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str = "."	)						->				int:




	'''simple docstring'''




	for dir_path, dir_names, filenames in os.walk(UpperCAmelCase_	):
		_UpperCamelCase   :		Dict			=						[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(UpperCAmelCase_	)[1] in (".py", ".ipynb"):
				yield os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	).lstrip('./'	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]	)						->				Dict:




	'''simple docstring'''




	return F'''{i * "  "}*''' if i else "\n##"






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						old_path.split(os.sep	)
	for i, new_part in enumerate(new_path.split(os.sep	)	):
		if (i + 1 > len(UpperCAmelCase_	) or old_parts[i] != new_part) and new_part:
			print(F'''{md_prefix(UpperCAmelCase_	)} {new_part.replace("_"							,       " "	).title()}'''	)
	return new_path






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str = "."	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						""""""
	for filepath in sorted(good_file_paths(UpperCAmelCase_	)	):
		_UpperCamelCase   :		int			=						os.path.split(UpperCAmelCase_	)
		if filepath != old_path:
			_UpperCamelCase   :		Optional[int]			=						print_path(UpperCAmelCase_							,       UpperCAmelCase_	)
		_UpperCamelCase   :		Optional[int]			=						(filepath.count(os.sep	) + 1) if filepath else 0
		_UpperCamelCase   :		str			=						F'''{filepath}/{filename}'''.replace(' '							,       '%20'	)
		_UpperCamelCase   :		Optional[Any]			=						os.path.splitext(filename.replace('_'							,       ' '	).title()	)[0]
		print(F'''{md_prefix(UpperCAmelCase_	)} [{filename}]({url})'''	)


if __name__ == "__main__":
							print_directory_md(""".""")







import argparse

import torch

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


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

lowerCAmelCase__								=		[
    ["""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 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




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

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




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

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


if __name__ == "__main__":
							lowerCAmelCase__								=		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"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




