


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
   from transformers import LlamaTokenizerFast
except ImportError as e:
   warnings.warn(e)
   warnings.warn(
       "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
   )
   _snake_case          =							None
_snake_case          =							{
    "7B": 1_1008,
    "13B": 1_3824,
    "30B": 1_7920,
    "65B": 2_2016,
    "70B": 2_8672,
}
_snake_case          =							{
    "7B": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}



def   snake_case						(				_a:     int		,   _a:     Optional[int]=1		,   _a:     List[str]=256							)->		Union[str, Any]:
      '''simple docstring'''


      return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3							)							) + multiple_of - 1) // multiple_of)



def   snake_case						(				_a:     Tuple							)->		List[str]:
      '''simple docstring'''


      with open(__snake_case		,   'r'							) as f:
            return json.load(__snake_case							)



def   snake_case						(				_a:     Tuple		,   _a:     Union[str, Any]							)->		int:
      '''simple docstring'''


      with open(__snake_case		,   'w'							) as f:
            json.dump(__snake_case		,   __snake_case							)



def   snake_case						(				_a:     List[str]		,   _a:     Dict		,   _a:     Dict		,   _a:     Optional[int]=True							)->		List[Any]:
      '''simple docstring'''


      os.makedirs(__snake_case		,   exist_ok=__snake_case							)
      lowerCamelCase__           =		os.path.join(__snake_case		,   'tmp'							)
      os.makedirs(__snake_case		,   exist_ok=__snake_case							)

      lowerCamelCase__           =		read_json(os.path.join(__snake_case		,   'params.json'							)							)
      lowerCamelCase__           =		NUM_SHARDS[model_size]
      lowerCamelCase__           =		params['n_layers']
      lowerCamelCase__           =		params['n_heads']
      lowerCamelCase__           =		n_heads // num_shards
      lowerCamelCase__           =		params['dim']
      lowerCamelCase__           =		dim // n_heads
      lowerCamelCase__           =		10000.0
      lowerCamelCase__           =		1.0 / (base ** (torch.arange(0		,   __snake_case		,   2							).float() / dims_per_head))

      if "n_kv_heads" in params:
            lowerCamelCase__           =		params['n_kv_heads']  # for GQA / MQA
            lowerCamelCase__           =		n_heads_per_shard // num_key_value_heads
            lowerCamelCase__           =		dim // num_key_value_heads
      else:  # compatibility with other checkpoints
            lowerCamelCase__           =		n_heads
            lowerCamelCase__           =		n_heads_per_shard
            lowerCamelCase__           =		dim

      # permute for sliced rotary
      def permute(_a:     Dict		,   _a:     Tuple=n_heads		,   _a:     Any=dim		,   _a:     Union[str, Any]=dim							):
            return w.view(__snake_case		,   dima // n_heads // 2		,   2		,   __snake_case							).transpose(1		,   2							).reshape(__snake_case		,   __snake_case							)

      print(F'Fetching all parameters from the checkpoint at {input_base_path}.'							)
      # Load weights
      if model_size == "7B":
            # Not sharded
            # (The sharded implementation would also work, but this is simpler.)
            lowerCamelCase__           =		torch.load(os.path.join(__snake_case		,   'consolidated.00.pth'							)		,   map_location='cpu'							)
      else:
            # Sharded
            lowerCamelCase__           =		[
                torch.load(os.path.join(__snake_case		,   F'consolidated.{i:02d}.pth'							)		,   map_location='cpu'							)
                for i in range(__snake_case							)
            ]
      lowerCamelCase__           =		0
      lowerCamelCase__           =		{'weight_map': {}}
      for layer_i in range(__snake_case							):
            lowerCamelCase__           =		F'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
            if model_size == "7B":
                  # Unsharded
                  lowerCamelCase__           =		{
                      F'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
                          loaded[F'layers.{layer_i}.attention.wq.weight']							),
                      F'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
                          loaded[F'layers.{layer_i}.attention.wk.weight']							),
                      F'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[F'layers.{layer_i}.attention.wv.weight'],
                      F'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[F'layers.{layer_i}.attention.wo.weight'],
                      F'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w1.weight'],
                      F'model.layers.{layer_i}.mlp.down_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w2.weight'],
                      F'model.layers.{layer_i}.mlp.up_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w3.weight'],
                      F'model.layers.{layer_i}.input_layernorm.weight': loaded[F'layers.{layer_i}.attention_norm.weight'],
                      F'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[F'layers.{layer_i}.ffn_norm.weight'],
                  }
            else:
                  # Sharded
                  # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
                  # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
                  # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

                  lowerCamelCase__           =		{
                      F'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
                          F'layers.{layer_i}.attention_norm.weight'
                      ].clone(),
                      F'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
                          F'layers.{layer_i}.ffn_norm.weight'
                      ].clone(),
                  }
                  lowerCamelCase__           =		permute(
                      torch.cat(
                          [
                              loaded[i][F'layers.{layer_i}.attention.wq.weight'].view(__snake_case		,   __snake_case		,   __snake_case							)
                              for i in range(__snake_case							)
                          ]		,   dim=0		,   ).reshape(__snake_case		,   __snake_case							)							)
                  lowerCamelCase__           =		permute(
                      torch.cat(
                          [
                              loaded[i][F'layers.{layer_i}.attention.wk.weight'].view(
                                  __snake_case		,   __snake_case		,   __snake_case							)
                              for i in range(__snake_case							)
                          ]		,   dim=0		,   ).reshape(__snake_case		,   __snake_case							)		,   __snake_case		,   __snake_case		,   __snake_case		,   )
                  lowerCamelCase__           =		torch.cat(
                      [
                          loaded[i][F'layers.{layer_i}.attention.wv.weight'].view(
                              __snake_case		,   __snake_case		,   __snake_case							)
                          for i in range(__snake_case							)
                      ]		,   dim=0		,   ).reshape(__snake_case		,   __snake_case							)

                  lowerCamelCase__           =		torch.cat(
                      [loaded[i][F'layers.{layer_i}.attention.wo.weight'] for i in range(__snake_case							)]		,   dim=1							)
                  lowerCamelCase__           =		torch.cat(
                      [loaded[i][F'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__snake_case							)]		,   dim=0							)
                  lowerCamelCase__           =		torch.cat(
                      [loaded[i][F'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__snake_case							)]		,   dim=1							)
                  lowerCamelCase__           =		torch.cat(
                      [loaded[i][F'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__snake_case							)]		,   dim=0							)

            lowerCamelCase__           =		inv_freq
            for k, v in state_dict.items():
                  lowerCamelCase__           =		filename
                  param_count += v.numel()
            torch.save(__snake_case		,   os.path.join(__snake_case		,   __snake_case							)							)

      lowerCamelCase__           =		F'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
      if model_size == "7B":
            # Unsharded
            lowerCamelCase__           =		{
                'model.embed_tokens.weight': loaded['tok_embeddings.weight'],
                'model.norm.weight': loaded['norm.weight'],
                'lm_head.weight': loaded['output.weight'],
            }
      else:
            lowerCamelCase__           =		{
                'model.norm.weight': loaded[0]['norm.weight'],
                'model.embed_tokens.weight': torch.cat(
                    [loaded[i]['tok_embeddings.weight'] for i in range(__snake_case							)]		,   dim=1							),
                'lm_head.weight': torch.cat([loaded[i]['output.weight'] for i in range(__snake_case							)]		,   dim=0							),
            }

      for k, v in state_dict.items():
            lowerCamelCase__           =		filename
            param_count += v.numel()
      torch.save(__snake_case		,   os.path.join(__snake_case		,   __snake_case							)							)

      # Write configs
      lowerCamelCase__           =		{'total_size': param_count * 2}
      write_json(__snake_case		,   os.path.join(__snake_case		,   'pytorch_model.bin.index.json'							)							)
      lowerCamelCase__           =		params['ffn_dim_multiplier'] if 'ffn_dim_multiplier' in params else 1
      lowerCamelCase__           =		params['multiple_of'] if 'multiple_of' in params else 256
      lowerCamelCase__           =		LlamaConfig(
          hidden_size=__snake_case		,   intermediate_size=compute_intermediate_size(__snake_case		,   __snake_case		,   __snake_case							)		,   num_attention_heads=params['n_heads']		,   num_hidden_layers=params['n_layers']		,   rms_norm_eps=params['norm_eps']		,   num_key_value_heads=__snake_case		,   )
      config.save_pretrained(__snake_case							)

      # Make space so we can load the model properly now.
      del state_dict
      del loaded
      gc.collect()

      print('Loading the checkpoint in a Llama model.'							)
      lowerCamelCase__           =		LlamaForCausalLM.from_pretrained(__snake_case		,   torch_dtype=torch.floataa		,   low_cpu_mem_usage=__snake_case							)
      # Avoid saving this as part of the config.
      del model.config._name_or_path

      print('Saving in the Transformers format.'							)
      model.save_pretrained(__snake_case		,   safe_serialization=__snake_case							)
      shutil.rmtree(__snake_case							)



def   snake_case						(				_a:     List[str]		,   _a:     Optional[int]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
      print(F'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'							)
      lowerCamelCase__           =		tokenizer_class(__snake_case							)
      tokenizer.save_pretrained(__snake_case							)



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()
      parser.add_argument(
          '--input_dir'		,   help='Location of LLaMA weights, which contains tokenizer.model and model folders'		,   )
      parser.add_argument(
          '--model_size'		,   choices=['7B', '7Bf', '13B', '13Bf', '30B', '65B', '70B', '70Bf', 'tokenizer_only']		,   )
      parser.add_argument(
          '--output_dir'		,   help='Location to write HF model and tokenizer'		,   )
      parser.add_argument('--safe_serialization'		,   type=__snake_case		,   help='Whether or not to save using `safetensors`.'							)
      lowerCamelCase__           =		parser.parse_args()
      if args.model_size != "tokenizer_only":
            write_model(
                model_path=args.output_dir		,   input_base_path=os.path.join(args.input_dir		,   args.model_size							)		,   model_size=args.model_size		,   safe_serialization=args.safe_serialization		,   )
      lowerCamelCase__           =		os.path.join(args.input_dir		,   'tokenizer.model'							)
      write_tokenizer(args.output_dir		,   __snake_case							)


if __name__ == "__main__":
   main()







"""simple docstring"""


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      lowerCamelCase__           =		True

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

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

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

            max_flow += path_flow
            lowerCamelCase__           =		sink

            while v != source:
                  lowerCamelCase__           =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase__           =		parent[v]
      return max_flow


_snake_case          =							[
    [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],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



