

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

a_      =     TypeVar('KEY')
a_      =     TypeVar('VAL')






@dataclass(frozen=__A				,						slots=__A )
class _UpperCamelCase				(				Generic[KEY, VAL] ):
			'''simple docstring'''







			lowerCamelCase__							     =42
			lowerCamelCase__							     =42






class _UpperCamelCase				(				_Item ):
			'''simple docstring'''







			def __init__(      self				:			Dict						)				->							None:

				"""simple docstring"""

				super().__init__(a   ,   a						)






			def __bool__(      self				:			str						)				->							bool:

				"""simple docstring"""

				return False

a_      =     _DeletedItem()






class _UpperCamelCase				(				MutableMapping[KEY, VAL] ):
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			int = 8   ,   a				:			float = 0.75						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							initial_block_size
				SCREAMING_SNAKE_CASE :   list[_Item | None]					  =							[None] * initial_block_size
				assert 0.0 < capacity_factor < 1.0
				SCREAMING_SNAKE_CASE :   List[str]					  =							capacity_factor
				SCREAMING_SNAKE_CASE :   List[Any]					  =							0
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			KEY						)				->							int:

				"""simple docstring"""

				return hash(a						) % len(self._buckets						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return (ind + 1) % len(self._buckets						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			int   ,   a				:			KEY   ,   a				:			VAL						)				->							bool:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self._buckets[ind]
				if not stored:
					SCREAMING_SNAKE_CASE :   int					  =							_Item(a   ,   a						)
					self._len += 1
					return True
				elif stored.key == key:
					SCREAMING_SNAKE_CASE :   Dict					  =							_Item(a   ,   a						)
					return True
				else:
					return False
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							bool:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							len(self._buckets						) * self._capacity_factor
				return len(self						) >= int(a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							bool:

				"""simple docstring"""

				if len(self._buckets						) <= self._initial_block_size:
					return False
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							len(self._buckets						) * self._capacity_factor / 2
				return len(self						) < limit
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			int						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self._buckets
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[None] * new_size
				SCREAMING_SNAKE_CASE :   Dict					  =							0
				for item in old_buckets:
					if item:
						self._add_item(item.key   ,   item.val						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							None:

				"""simple docstring"""

				self._resize(len(self._buckets						) * 2						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							None:

				"""simple docstring"""

				self._resize(len(self._buckets						) // 2						)
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			KEY						)				->							Iterator[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self._get_bucket_index(a						)
				for _ in range(len(self._buckets						)						):
					yield ind
					SCREAMING_SNAKE_CASE :   int					  =							self._get_next_ind(a						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			KEY   ,   a				:			VAL						)				->							None:

				"""simple docstring"""

				for ind in self._iterate_buckets(a						):
					if self._try_set(a   ,   a   ,   a						):
						break
			def __setitem__(      self				:			List[str]   ,   a				:			KEY   ,   a				:			VAL						)				->							None:

				"""simple docstring"""

				if self._is_full():
					self._size_up()

				self._add_item(a   ,   a						)
			def __delitem__(      self				:			int   ,   a				:			KEY						)				->							None:

				"""simple docstring"""

				for ind in self._iterate_buckets(a						):
					SCREAMING_SNAKE_CASE :   List[str]					  =							self._buckets[ind]
					if item is None:
						raise KeyError(a						)
					if item is _deleted:
						continue
					if item.key == key:
						SCREAMING_SNAKE_CASE :   int					  =							_deleted
						self._len -= 1
						break
				if self._is_sparse():
					self._size_down()
			def __getitem__(      self				:			List[str]   ,   a				:			KEY						)				->							VAL:

				"""simple docstring"""

				for ind in self._iterate_buckets(a						):
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._buckets[ind]
					if item is None:
						break
					if item is _deleted:
						continue
					if item.key == key:
						return item.val
				raise KeyError(a						)
			def __len__(      self				:			Optional[Any]						)				->							int:

				"""simple docstring"""

				return self._len
			def __iter__(      self				:			Any						)				->							Iterator[KEY]:

				"""simple docstring"""

				yield from (item.key for item in self._buckets if item)






			def __repr__(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							" ,".join(
				    F"{item.key}: {item.val}" for item in self._buckets if item						)
				return F"HashMap({val_string})"



import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["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__:  str    )			-> int:





	"""simple docstring"""







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

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					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__":
				_lowercase							:   str						=						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"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



