


import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							[
    ['''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       _snake_case     (  A )		->					Optional[Any]:
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														lowerCAmelCase__      =			k.replace(_lowerCamelCase				,					_lowerCamelCase )

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


def       _snake_case     (  A )		->					Any:
							lowerCAmelCase__      =			[
							    """model.encoder.layernorm_embedding.weight""",
							    """model.encoder.layernorm_embedding.bias""",
							    """model.decoder.layernorm_embedding.weight""",
							    """model.decoder.layernorm_embedding.bias""",
							]
							for k in keys:
														lowerCAmelCase__      =			sd.pop(_lowerCamelCase )
														lowerCAmelCase__      =			k.replace('''layernorm_embedding'''				,					'''layer_norm''' )
														assert new_k not in sd
														lowerCAmelCase__      =			v


__UpperCAmelCase					       =							['''START''']


@torch.no_grad()
def       _snake_case     (  A				,					A				,					A )		->					Optional[Any]:
							lowerCAmelCase__      =			torch.load(_lowerCamelCase				,					map_location='''cpu''' )
							lowerCAmelCase__      =			model["""model"""]
							lowerCAmelCase__      =			BlenderbotConfig.from_json_file(_lowerCamelCase )
							lowerCAmelCase__      =			BlenderbotForConditionalGeneration(_lowerCamelCase )
							lowerCAmelCase__      =			m.model.state_dict().keys()
							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

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


if __name__ == "__main__":
		__UpperCAmelCase					       =							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					       =							parser.parse_args()
		convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig