


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


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





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	A             =					r"""\w+[.]\d+"""
	A             =					re.findall(UpperCamelCase__				,       UpperCamelCase__    )
	for pat in pats:
		A             =					key.replace(UpperCamelCase__				,       """_""".join(pat.split("""."""    )    )    )
	return key





def 	_lowerCAmelCase					( UpperCamelCase__:  int				,       UpperCamelCase__:  int				,       UpperCamelCase__:  List[Any]    )			-> Tuple:





	"""simple docstring"""







	A             =					pt_tuple_key[:-1] + ("""scale""",)
	if (
	    any("""norm""" in str_ for str_ in pt_tuple_key    )
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		A             =					pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		A             =					pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		A             =					pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	A             =					pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		A             =					pt_tensor.transpose(2				,       3				,       1				,       0    )
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	A             =					pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight":
		A             =					pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	A             =					pt_tuple_key[:-1] + ("""weight""",)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	A             =					pt_tuple_key[:-1] + ("""bias""",)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor





def 	_lowerCAmelCase					( UpperCamelCase__:  int				,       UpperCamelCase__:  Dict				,       UpperCamelCase__:  Tuple=42    )			-> Any:





	"""simple docstring"""







	A             =					{k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	A             =					flax_model.init_weights(PRNGKey(UpperCamelCase__    )    )

	A             =					flatten_dict(UpperCamelCase__    )
	A             =					{}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		A             =					rename_key(UpperCamelCase__    )
		A             =					tuple(renamed_pt_key.split("""."""    )    )

		# Correctly rename weight parameters
		A       ,      A             =					rename_key_and_reshape_tensor(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
				    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )

        # also add unexpected weight so that warning is thrown
		A             =					jnp.asarray(UpperCamelCase__    )

	return unflatten_dict(UpperCamelCase__    )







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)



