import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	emb.weight.shape
    __UpperCamelCase  :Optional[int]    						=	nn.Linear(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    bias=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :int    						=	emb.weight.data
    return lin_layer



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	{}
    for old_key in state_dict.keys():
        __UpperCamelCase  :int    						=	old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                __UpperCamelCase  :Union[str, Any]    						=	key.replace('''moe_layer.experts.0'''						,    f"""ffn.experts.expert_{expert_idx}"""			)
            else:
                __UpperCamelCase  :Any    						=	key.replace('''moe_layer.experts.'''						,    '''ffn.experts.expert_'''			)
        if "gate" in key:
            __UpperCamelCase  :List[str]    						=	key.replace('''.moe_layer.gate.wg'''						,    '''.ffn.router.classifier'''			)
        if "fc2" and "experts" not in key:
            __UpperCamelCase  :str    						=	key.replace('''.fc2.'''						,    '''.ffn.fc2.'''			)
        if "fc1" and "experts" not in key:
            __UpperCamelCase  :Union[str, Any]    						=	key.replace('''.fc1.'''						,    '''.ffn.fc1.'''			)
        if ".encoder_attn." in key:
            __UpperCamelCase  :Tuple    						=	key.replace('''.encoder_attn.'''						,    '''.cross_attention.'''			)
        if "encoder_attn_layer_norm" in key:
            __UpperCamelCase  :Union[str, Any]    						=	key.replace('''encoder_attn_layer_norm'''						,    '''cross_attention_layer_norm'''			)
        if "final_layer_norm" in key:
            __UpperCamelCase  :Union[str, Any]    						=	key.replace('''final_layer_norm'''						,    '''ff_layer_norm'''			)
        __UpperCamelCase  :int    						=	state_dict[old_key]
    return new_dict



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = WEIGHTS_NAME			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	[]
    __UpperCamelCase  :int    						=	0
    os.makedirs(SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE			)

    for expert in range(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :str    						=	switch_checkpoint_path + f"""-rank-{expert}.pt"""
        if os.path.isfile(SCREAMING_SNAKE_CASE			):
            __UpperCamelCase  :str    						=	torch.load(SCREAMING_SNAKE_CASE			)['''model''']
            remove_ignore_keys_(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :int    						=	rename_fairseq_keys(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[int]    						=	os.path.join(
                SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f"""-{len(SCREAMING_SNAKE_CASE			)+1:05d}-of-???.bin"""			)			)
            torch.save(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
            sharded_state_dicts.append(expert_state.keys()			)
            total_size += sum([value.numel() for key, value in expert_state.items()]			) * dtype_byte_size(
                expert_state[list(SCREAMING_SNAKE_CASE			)[0]].dtype			)

    # Add the last block
    __UpperCamelCase  :Optional[Any]    						=	os.path.join(SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f"""-{len(SCREAMING_SNAKE_CASE			)+1:05d}-of-???.bin"""			)			)
    __UpperCamelCase  :Union[str, Any]    						=	torch.load(switch_checkpoint_path + '''-shared.pt'''			)['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[str]    						=	rename_fairseq_keys(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	shared_weights['''decoder.embed_tokens.weight''']
    sharded_state_dicts.append(shared_weights.keys()			)

    # If we only have the shared weights (dummy model/experts saved on the same file)
    if len(SCREAMING_SNAKE_CASE			) == 1:
        __UpperCamelCase  :List[str]    						=	os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
        torch.save(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # Otherwise, let's build the index
    __UpperCamelCase  :Optional[Any]    						=	{}
    for idx, shard in enumerate(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	weights_name.replace('''.bin'''						,    f"""-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE			):05d}.bin"""			)
        __UpperCamelCase  :List[Any]    						=	os.path.join(SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f"""-{idx+1:05d}-of-???.bin"""			)			)
        os.rename(SCREAMING_SNAKE_CASE						,    os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)			)
        for key in shard:
            __UpperCamelCase  :Optional[Any]    						=	shard_file

    # Add the metadata
    __UpperCamelCase  :Dict    						=	{'''total_size''': total_size}
    __UpperCamelCase  :str    						=	{'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    '''w'''						,    encoding='''utf-8'''			) as f:
        __UpperCamelCase  :Union[str, Any]    						=	json.dumps(SCREAMING_SNAKE_CASE						,    indent=2						,    sort_keys=SCREAMING_SNAKE_CASE			) + '''\n'''
        f.write(SCREAMING_SNAKE_CASE			)

    return metadata, index


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--nllb_moe_checkpoint_path''',
        default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
        type=str,
        required=False,
        help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
    )
    parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
        type=str,
        required=False,
        help='''Path to the output pytorch model.''',
    )
    __lowercase											=      parser.parse_args()
    __lowercase				, __lowercase											=      shard_on_the_fly(
        args.nllb_moe_checkpoint_path,
        args.pytorch_dump_folder_path,
        128,
        args.dtype,
    )

    __lowercase											=      NllbMoeConfig.from_pretrained(
        '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
    )
    config.save_pretrained(args.pytorch_dump_folder_path)
    __lowercase											=      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
    print('''Done''')
    model.save_pretrained(args.pytorch_dump_folder_path)






from __future__ import annotations

import bisect



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :str    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[int]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  :List[Any]    						=	mid + 1
        else:
            __UpperCamelCase  :Any    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[Any]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  :Dict    						=	mid + 1
        else:
            __UpperCamelCase  :Optional[Any]    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			) - 1

    while left <= right:
        __UpperCamelCase  :Optional[int]    						=	left + (right - left) // 2
        __UpperCamelCase  :List[str]    						=	sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  :Union[str, Any]    						=	midpoint - 1
        else:
            __UpperCamelCase  :Dict    						=	midpoint + 1
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bisect.bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    if index != len(SCREAMING_SNAKE_CASE			) and sorted_collection[index] == item:
        return index
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if right < left:
        return None

    __UpperCamelCase  :str    						=	left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint - 1			)
    else:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint + 1						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      input('''Enter numbers separated by comma:\n''').strip()
    __lowercase											=      sorted(int(item) for item in user_input.split(''','''))
    __lowercase											=      int(input('''Enter a single number to be found in the list:\n'''))
    __lowercase											=      binary_search(collection, target)
    if result is None:
        print(F'{target} was not found in {collection}.')
    else:
        print(F'{target} was found at position {result} in {collection}.')





