


import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: 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 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    if k == "embeddings.weight":
        return "shared.weight"

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

    if k.startswith('encoder'					):
        _lowercase			:				int 						=						k.replace('.attn'				,					'.self_attn'					)
        _lowercase			:				int 						=						k.replace('norm1'				,					'self_attn_layer_norm'					)
        _lowercase			:				int 						=						k.replace('norm2'				,					'final_layer_norm'					)
    elif k.startswith('decoder'					):
        _lowercase			:				Tuple 						=						k.replace('norm1'				,					'self_attn_layer_norm'					)
        _lowercase			:				str 						=						k.replace('norm2'				,					'encoder_attn_layer_norm'					)
        _lowercase			:				str 						=						k.replace('norm3'				,					'final_layer_norm'					)
    return k



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[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:
        _lowercase			:				Tuple 						=						sd.pop(A__					)
        _lowercase			:				str 						=						k.replace('layernorm_embedding'				,					'layer_norm'					)
        assert new_k not in sd
        _lowercase			:				Dict 						=						v


SCREAMING_SNAKE_CASE			: Tuple				   =					['START']



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    _lowercase			:				Optional[int] 						=						torch.load(A__				,					map_location='cpu'					)
    _lowercase			:				int 						=						model['model']
    _lowercase			:				List[str] 						=						BlenderbotConfig.from_json_file(A__					)
    _lowercase			:				Optional[Any] 						=						BlenderbotForConditionalGeneration(A__					)
    _lowercase			:				str 						=						m.model.state_dict().keys()
    _lowercase			:				int 						=						[]
    _lowercase			:				List[str] 						=						{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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"
 )
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)