



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


__snake_case		:str   	=  {
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

__snake_case		:Optional[int]   	=  {
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}





def   __snake_case			(    _UpperCAmelCase       ):
				__a		 =       list(state_dict.keys()       )
				for name in state_dict_keys:
								__a		 =       state_dict.pop(_UpperCAmelCase       )
								# emb -> embedding
								if name.startswith('''emb.'''       ):
												__a		 =       name.replace('''emb.'''     ,				'''embeddings.'''       )
								# ln_0 -> pre_ln (only present at block 0)
								if name.startswith('''blocks.0.ln0'''       ):
												__a		 =       name.replace('''blocks.0.ln0'''     ,				'''blocks.0.pre_ln'''       )
								# att -> attention
								__a		 =       re.sub(R'''blocks\.(\d+)\.att'''     ,				R'''blocks.\1.attention'''     ,				_UpperCAmelCase       )
								# ffn -> feed_forward
								__a		 =       re.sub(R'''blocks\.(\d+)\.ffn'''     ,				R'''blocks.\1.feed_forward'''     ,				_UpperCAmelCase       )
								# time_mix_k -> time_mix_key and reshape
								if name.endswith('''.time_mix_k'''       ):
												__a		 =       name.replace('''.time_mix_k'''     ,				'''.time_mix_key'''       )
								# time_mix_v -> time_mix_value and reshape
								if name.endswith('''.time_mix_v'''       ):
												__a		 =       name.replace('''.time_mix_v'''     ,				'''.time_mix_value'''       )
								# time_mix_r -> time_mix_key and reshape
								if name.endswith('''.time_mix_r'''       ):
												__a		 =       name.replace('''.time_mix_r'''     ,				'''.time_mix_receptance'''       )

								if name != "head.weight":
												__a		 =       '''rwkv.''' + name

								__a		 =       weight
				return state_dict





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=None       ):
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
								__a		 =       50277
								__a		 =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
				else:
								__a		 =       PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase       )
								__a		 =       len(_UpperCAmelCase       )
				tokenizer.save_pretrained(_UpperCAmelCase       )

				# 2. Build the config
				__a		 =       list(NUM_HIDDEN_LAYERS_MAPPING.keys()       )
				if size is None:
								# Try to infer size from the checkpoint name
								for candidate in possible_sizes:
												if candidate in checkpoint_file:
																__a		 =       candidate
																break
								if size is None:
												raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''       )
				if size not in possible_sizes:
								raise ValueError(f'`size` should be one of {possible_sizes}, got {size}.'       )

				__a		 =       RwkvConfig(
				    vocab_size=_UpperCAmelCase     ,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,				hidden_size=HIDEN_SIZE_MAPPING[size]     ,				)
				config.save_pretrained(_UpperCAmelCase       )

				# 3. Download model file then convert state_dict
				__a		 =       hf_hub_download(_UpperCAmelCase     ,				_UpperCAmelCase       )
				__a		 =       torch.load(_UpperCAmelCase     ,				map_location='''cpu'''       )
				__a		 =       convert_state_dict(_UpperCAmelCase       )

				# 4. Split in shards and save
				__a ,     __a		 =       shard_checkpoint(_UpperCAmelCase       )
				for shard_file, shard in shards.items():
								torch.save(_UpperCAmelCase     ,				os.path.join(_UpperCAmelCase     ,				_UpperCAmelCase       )       )

				if index is not None:
								__a		 =       os.path.join(_UpperCAmelCase     ,				_UpperCAmelCase       )
								# Save the index as well
								with open(_UpperCAmelCase     ,				'''w'''     ,				encoding='''utf-8'''       ) as f:
												__a		 =       json.dumps(_UpperCAmelCase     ,				indent=2     ,				sort_keys=_UpperCAmelCase       ) + '''\n'''
												f.write(_UpperCAmelCase       )

								# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
								print(
								    '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''       )
								__a		 =       list(shards.keys()       )

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												__a		 =       torch.load(os.path.join(_UpperCAmelCase     ,				_UpperCAmelCase       )       )
												torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,				os.path.join(_UpperCAmelCase     ,				_UpperCAmelCase       )       )

				del state_dict
				gc.collect()

				if push_to_hub:
								if model_name is None:
												raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''       )
								__a		 =       AutoModelForCausalLM.from_pretrained(_UpperCAmelCase       )
								model.push_to_hub(_UpperCAmelCase     ,				max_shard_size='''2GB'''       )
								tokenizer.push_to_hub(_UpperCAmelCase       )


if __name__ == "__main__":
	__snake_case		:Tuple   	=  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
	)
	parser.add_argument(
	    '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
	)
	parser.add_argument(
	    '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
	)
	parser.add_argument(
	    '''--tokenizer_file''',
	    default=None,
	    type=str,
	    help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
	)
	parser.add_argument(
	    '''--size''',
	    default=None,
	    type=str,
	    help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	    help='''Push to the Hub the converted model.''',
	)
	parser.add_argument(
	    '''--model_name''',
	    default=None,
	    type=str,
	    help='''Name of the pushed model on the Hub, including the username / organization.''',
	)

	__snake_case		:int   	=  parser.parse_args()
	convert_rmkv_checkpoint_to_hf_format(
	    args.repo_id,
	    args.checkpoint_file,
	    args.output_dir,
	    size=args.size,
	    tokenizer_file=args.tokenizer_file,
	    push_to_hub=args.push_to_hub,
	    model_name=args.model_name,
	)










__snake_case		:str   	=  [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Return True if there is node that has not iterated.
    __a		 =       [False] * len(_UpperCAmelCase       )
    __a		 =       [s]
    __a		 =       True

    while queue:
        __a		 =       queue.pop(0       )
        for ind in range(len(graph[u]       )       ):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_UpperCAmelCase       )
                __a		 =       True
                __a		 =       u

    return visited[t]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [-1] * (len(_UpperCAmelCase       ))
    __a		 =       0
    __a		 =       []
    __a		 =       [i[:] for i in graph]  # Record original cut, copy.
    while bfs(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       float('''Inf'''       )
        __a		 =       sink

        while s != source:
            # Find the minimum value in select path
            __a		 =       min(_UpperCAmelCase     ,				graph[parent[s]][s]       )
            __a		 =       parent[s]

        max_flow += path_flow
        __a		 =       sink

        while v != source:
            __a		 =       parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            __a		 =       parent[v]

    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(graph[0]       )       ):
            if graph[i][j] == 0 and temp[i][j] > 0:
                res.append((i, j)       )

    return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))





