





from typing import List

import numpy as np
def 		__magic_name__				( lowercase_				)		->					Any:


						'''simple docstring'''


						UpperCamelCase  							= {key: len(UpperCamelCase_				) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase_						,		UpperCamelCase_				)}
						if len(set(lists_lengths.values()				)				) > 1:
												raise RuntimeError(
												    (
												        "Sharding is ambiguous for this dataset: "
												        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
												        + "\n".join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()				)
												        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
												        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
												    )				)
						UpperCamelCase  							= max(lists_lengths.values()						,		default=0				)
						return max(1						,		UpperCamelCase_				)
def 		__magic_name__				( lowercase_						,		lowercase_				)		->					int:


						'''simple docstring'''


						UpperCamelCase  							= []
						for group_idx in range(UpperCamelCase_				):
												UpperCamelCase  							= num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
												if num_shards_to_add == 0:
																		break
												UpperCamelCase  							= shards_indices_per_group[-1].stop if shards_indices_per_group else 0
												UpperCamelCase  							= range(UpperCamelCase_						,		start + num_shards_to_add				)
												shards_indices_per_group.append(UpperCamelCase_				)
						return shards_indices_per_group
def 		__magic_name__				( lowercase_						,		lowercase_				)		->					Union[str, Any]:


						'''simple docstring'''


						UpperCamelCase  							= _number_of_shards_in_gen_kwargs(UpperCamelCase_				)
						if num_shards == 1:
												return [dict(UpperCamelCase_				)]
						else:
												UpperCamelCase  							= _distribute_shards(num_shards=UpperCamelCase_						,		max_num_jobs=UpperCamelCase_				)
												return [
												    {
												        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
												        if isinstance(UpperCamelCase_						,		UpperCamelCase_				)
												        else value
												        for key, value in gen_kwargs.items()
												    }
												    for group_idx in range(len(UpperCamelCase_				)				)
												]
def 		__magic_name__				( lowercase_				)		->					Any:


						'''simple docstring'''


						return {
						    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
						    if isinstance(gen_kwargs_list[0][key]						,		UpperCamelCase_				)
						    else gen_kwargs_list[0][key]
						    for key in gen_kwargs_list[0]
						}





def 		__magic_name__				( lowercase_						,		lowercase_				)		->					List[Any]:


						'''simple docstring'''


						UpperCamelCase  							= {len(UpperCamelCase_				) for value in gen_kwargs.values() if isinstance(UpperCamelCase_						,		UpperCamelCase_				)}
						UpperCamelCase  							= {}
						for size in list_sizes:
												UpperCamelCase  							= list(range(UpperCamelCase_				)				)
												rng.shuffle(indices_per_size[size]				)
						# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
						UpperCamelCase  							= dict(UpperCamelCase_				)
						for key, value in shuffled_kwargs.items():
												if isinstance(UpperCamelCase_						,		UpperCamelCase_				):
																		UpperCamelCase  							= [value[i] for i in indices_per_size[len(UpperCamelCase_				)]]
						return shuffled_kwargs








'''simple docstring'''
import argparse

import torch

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


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

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    ['''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:
														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       a_				(	UpperCamelCase_ ):
							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


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							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__":
							__SCREAMING_SNAKE_CASE					:				Union[str, 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'''
							)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





