

import argparse

import torch

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


logging.set_verbosity_info()
_UpperCAmelCase	:     Union[str, Any]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     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__   ):







       '''simple docstring'''






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

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

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





def       __lowerCamelCase			(				UpperCamelCase__   ):







       '''simple docstring'''






       snake_case_           =					[
           '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_           =					sd.pop(UpperCamelCase__   )
              snake_case_           =					k.replace('layernorm_embedding'     ,							'layer_norm'   )
              assert new_k not in sd
              snake_case_           =					v


_UpperCAmelCase	:     Dict		       = ["""START"""]





@torch.no_grad()
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







       '''simple docstring'''






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

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







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)




