
"""simple docstring"""


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"""
						)
						A_    							=					None
A_    							=					{
    """7B""": 1_1008,
    """13B""": 1_3824,
    """30B""": 1_7920,
    """65B""": 2_2016,
    """70B""": 2_8672,
}
A_    							=					{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=1	,lowerCAmelCase__=256						):
			return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3						)						) + multiple_of - 1) // multiple_of)






def  lowercase						(	lowerCAmelCase__						):
			with open(lowerCAmelCase__	,'''r'''						) as f:
						return json.load(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			with open(lowerCAmelCase__	,'''w'''						) as f:
						json.dump(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=True						):
			os.makedirs(lowerCAmelCase__	,exist_ok=lowerCAmelCase__						)
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''tmp'''						)
			os.makedirs(lowerCAmelCase__	,exist_ok=lowerCAmelCase__						)

			lowerCamelCase_					=  read_json(os.path.join(lowerCAmelCase__	,'''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_					=  10_000.0
			lowerCamelCase_					=  1.0 / (base ** (torch.arange(0	,lowerCAmelCase__	,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(lowerCAmelCase__	,lowerCAmelCase__=n_heads	,lowerCAmelCase__=dim	,lowerCAmelCase__=dim						):
						return w.view(lowerCAmelCase__	,dima // n_heads // 2	,2	,lowerCAmelCase__						).transpose(1	,2						).reshape(lowerCAmelCase__	,lowerCAmelCase__						)

			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(lowerCAmelCase__	,'''consolidated.00.pth'''						)	,map_location='''cpu'''						)
			else:
						# Sharded
						lowerCamelCase_					=  [
						    torch.load(os.path.join(lowerCAmelCase__	,f"consolidated.{i:02d}.pth"						)	,map_location='''cpu'''						)
						    for i in range(lowerCAmelCase__						)
						]
			lowerCamelCase_					=  0
			lowerCamelCase_					=  {'''weight_map''': {}}
			for layer_i in range(lowerCAmelCase__						):
						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(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
									            for i in range(lowerCAmelCase__						)
									        ]	,dim=0	,).reshape(lowerCAmelCase__	,lowerCAmelCase__						)						)
									lowerCamelCase_					=  permute(
									    torch.cat(
									        [
									            loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
									                lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
									            for i in range(lowerCAmelCase__						)
									        ]	,dim=0	,).reshape(lowerCAmelCase__	,lowerCAmelCase__						)	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,)
									lowerCamelCase_					=  torch.cat(
									    [
									        loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
									            lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
									        for i in range(lowerCAmelCase__						)
									    ]	,dim=0	,).reshape(lowerCAmelCase__	,lowerCAmelCase__						)

									lowerCamelCase_					=  torch.cat(
									    [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(lowerCAmelCase__						)]	,dim=1						)
									lowerCamelCase_					=  torch.cat(
									    [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(lowerCAmelCase__						)]	,dim=0						)
									lowerCamelCase_					=  torch.cat(
									    [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(lowerCAmelCase__						)]	,dim=1						)
									lowerCamelCase_					=  torch.cat(
									    [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(lowerCAmelCase__						)]	,dim=0						)

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

			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(lowerCAmelCase__						)]	,dim=1						),
						    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(lowerCAmelCase__						)]	,dim=0						),
						}

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

			# Write configs
			lowerCamelCase_					=  {'''total_size''': param_count * 2}
			write_json(lowerCAmelCase__	,os.path.join(lowerCAmelCase__	,'''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=lowerCAmelCase__	,intermediate_size=compute_intermediate_size(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)	,num_attention_heads=params['''n_heads''']	,num_hidden_layers=params['''n_layers''']	,rms_norm_eps=params['''norm_eps''']	,num_key_value_heads=lowerCAmelCase__	,)
			config.save_pretrained(lowerCAmelCase__						)

			# 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(lowerCAmelCase__	,torch_dtype=torch.floataa	,low_cpu_mem_usage=lowerCAmelCase__						)
			# Avoid saving this as part of the config.
			del model.config._name_or_path

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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			# Initialize the tokenizer based on the `spm` model
			lowerCamelCase_					=  LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
			print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}."						)
			lowerCamelCase_					=  tokenizer_class(lowerCAmelCase__						)
			tokenizer.save_pretrained(lowerCAmelCase__						)






def  lowercase						(	):
			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=lowerCAmelCase__	,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	,lowerCAmelCase__						)


if __name__ == "__main__":
						main()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'mgp-str'







	def __init__(     self      :Union[str, Any]   ,     lowercase      :Union[str, Any]=[3_2, 1_2_8]   ,     lowercase      :Tuple=4   ,     lowercase      :Optional[int]=3   ,     lowercase      :Union[str, Any]=2_7   ,     lowercase      :List[str]=3_8   ,     lowercase      :Optional[Any]=5_0_2_5_7   ,     lowercase      :str=3_0_5_2_2   ,     lowercase      :Dict=7_6_8   ,     lowercase      :List[Any]=1_2   ,     lowercase      :List[str]=1_2   ,     lowercase      :Optional[Any]=4.0   ,     lowercase      :Optional[Any]=True   ,     lowercase      :str=False   ,     lowercase      :Optional[Any]=1e-5   ,     lowercase      :List[str]=0.0   ,     lowercase      :List[Any]=0.0   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Dict=False   ,     lowercase      :Optional[Any]=0.02   ,     **lowercase      :Optional[Any]   ,     )		->      Dict:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= max_token_length
				SCREAMING_SNAKE_CASE     						= num_character_labels
				SCREAMING_SNAKE_CASE     						= num_bpe_labels
				SCREAMING_SNAKE_CASE     						= num_wordpiece_labels
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= mlp_ratio
				SCREAMING_SNAKE_CASE     						= distilled
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= drop_rate
				SCREAMING_SNAKE_CASE     						= qkv_bias
				SCREAMING_SNAKE_CASE     						= attn_drop_rate
				SCREAMING_SNAKE_CASE     						= drop_path_rate
				SCREAMING_SNAKE_CASE     						= output_aa_attentions
				SCREAMING_SNAKE_CASE     						= initializer_range