

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase							:  Optional[Any]   	=						logging.get_logger(__name__)

lowerCAmelCase							:  str   	=						[
    ["""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_      (    _UpperCAmelCase    ):
						if k == "embeddings.weight":
												return "shared.weight"

						for parlai_name, hf_name in PATTERNS:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       k.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

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







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: 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:
												SCREAMING_SNAKE_CASE_: List[str]  		=       sd.pop(_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       k.replace("layernorm_embedding"				,	"layer_norm"    )
												assert new_k not in sd
												SCREAMING_SNAKE_CASE_: str  		=       v


lowerCAmelCase							:  str   	=						["""START"""]







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

												SCREAMING_SNAKE_CASE_: str  		=       rename_state_dict_key(_UpperCAmelCase    )
												if new_k not in valid_keys:
																		failures.append([k, new_k]    )
												else:
																		SCREAMING_SNAKE_CASE_: Dict  		=       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							:  Any   	=						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							:  Tuple   	=						parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)













'''simple docstring'''




import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    "kwargs, expected"			,     [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10       )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase			,     i + 1       ) for i in range(10       )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1       )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0			,     4       ), range(4			,     7       ), range(7			,     10       )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0			,     1       ), range(1			,     2       ), range(2			,     3       )]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _distribute_shards(**__lowercase       )
     assert out == expected
@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"			,     [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[Any]			,     __lowercase							:      int       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   _split_gen_kwargs(__lowercase			,     __lowercase       )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"			,     [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



     if expected is RuntimeError:
          with pytest.raises(__lowercase       ):
               _number_of_shards_in_gen_kwargs(__lowercase       )
     else:
          _UpperCAmelCase     							=   _number_of_shards_in_gen_kwargs(__lowercase       )
          assert out == expected




