





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    :					Union[str, Any] =      None
A    :					List[Any] =      {
    '''7B''': 1_1_0_0_8,
    '''13B''': 1_3_8_2_4,
    '''30B''': 1_7_9_2_0,
    '''65B''': 2_2_0_1_6,
    '''70B''': 2_8_6_7_2,
}
A    :					List[Any] =      {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}







def    __lowerCamelCase					(							__a   :List[Any]    ,     __a   :List[Any]=1    ,     __a   :Union[str, Any]=2_5_6    )		->				Any:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :Optional[Any]    )		->				List[str]:






		"""simple docstring"""

		with open(__a    ,     """r"""    ) as f:
				return json.load(__a    )







def    __lowerCamelCase					(							__a   :Union[str, Any]    ,     __a   :Any    )		->				Union[str, Any]:






		"""simple docstring"""

		with open(__a    ,     """w"""    ) as f:
				json.dump(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :List[Any]    ,     __a   :Dict    ,     __a   :List[str]    ,     __a   :int=True    )		->				str:






		"""simple docstring"""

		os.makedirs(__a    ,     exist_ok=__a    )
		A__        =   os.path.join(__a    ,     """tmp"""    )
		os.makedirs(__a    ,     exist_ok=__a    )

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

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

		# permute for sliced rotary
		def permute(__a   :List[Any]    ,     __a   :Tuple=n_heads    ,     __a   :int=dim    ,     __a   :int=dim    ):
				return w.view(__a    ,     dima // n_heads // 2    ,     2    ,     __a    ).transpose(1    ,     2    ).reshape(__a    ,     __a    )

		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.)
				A__        =   torch.load(os.path.join(__a    ,     """consolidated.00.pth"""    )    ,     map_location="""cpu"""    )
		else:
				# Sharded
				A__        =   [
				    torch.load(os.path.join(__a    ,     F'consolidated.{i:02d}.pth'    )    ,     map_location="""cpu"""    )
				    for i in range(__a    )
				]
		A__        =   0
		A__        =   {"""weight_map""": {}}
		for layer_i in range(__a    ):
				A__        =   F'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
				if model_size == "7B":
						# Unsharded
						A__        =   {
						    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.

						A__        =   {
						    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(),
						}
						A__        =   permute(
						    torch.cat(
						        [
						            loaded[i][F'layers.{layer_i}.attention.wq.weight'].view(__a    ,     __a    ,     __a    )
						            for i in range(__a    )
						        ]    ,     dim=0    ,     ).reshape(__a    ,     __a    )    )
						A__        =   permute(
						    torch.cat(
						        [
						            loaded[i][F'layers.{layer_i}.attention.wk.weight'].view(
						                __a    ,     __a    ,     __a    )
						            for i in range(__a    )
						        ]    ,     dim=0    ,     ).reshape(__a    ,     __a    )    ,     __a    ,     __a    ,     __a    ,     )
						A__        =   torch.cat(
						    [
						        loaded[i][F'layers.{layer_i}.attention.wv.weight'].view(
						            __a    ,     __a    ,     __a    )
						        for i in range(__a    )
						    ]    ,     dim=0    ,     ).reshape(__a    ,     __a    )

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

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

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

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

		# Write configs
		A__        =   {"""total_size""": param_count * 2}
		write_json(__a    ,     os.path.join(__a    ,     """pytorch_model.bin.index.json"""    )    )
		A__        =   params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
		A__        =   params["""multiple_of"""] if """multiple_of""" in params else 2_5_6
		A__        =   LlamaConfig(
		    hidden_size=__a    ,     intermediate_size=compute_intermediate_size(__a    ,     __a    ,     __a    )    ,     num_attention_heads=params["""n_heads"""]    ,     num_hidden_layers=params["""n_layers"""]    ,     rms_norm_eps=params["""norm_eps"""]    ,     num_key_value_heads=__a    ,     )
		config.save_pretrained(__a    )

		# 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."""    )
		A__        =   LlamaForCausalLM.from_pretrained(__a    ,     torch_dtype=torch.floataa    ,     low_cpu_mem_usage=__a    )
		# Avoid saving this as part of the config.
		del model.config._name_or_path

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







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[str]    )		->				Optional[Any]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							)		->				Optional[int]:






		"""simple docstring"""

		A__        =   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=__a    ,     help="""Whether or not to save using `safetensors`."""    )
		A__        =   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    ,     )
		A__        =   os.path.join(args.input_dir    ,     """tokenizer.model"""    )
		write_tokenizer(args.output_dir    ,     __a    )


if __name__ == "__main__":
					main()










import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A    :					Dict =      logging.get_logger(__name__)







def    __lowerCamelCase					(							__a   :int=None    ,     __a   :Optional[Any]=None    )		->				int:






		"""simple docstring"""

		return field(default_factory=lambda: default    ,     metadata=__a    )






@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    list_field(
			    default=[]   ,     metadata={
			        '''help''': (
			            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
			            ''' of all available models'''
			        )
			    }   ,     )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8]   ,     metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}    )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8, 32, 128, 512]   ,     metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}   ,     )

			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Use FP16 to accelerate inference.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Benchmark training of model'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Verbose memory tracing'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
			    }   ,     )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Trace memory line by line'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save result to a CSV file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save all print statements in a log file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to print environment information'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
			            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
			            ''' for debugging / testing and on TPU.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''env_info_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving environment information.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''log_{round(time()    )}.csv'''   ,     metadata={'''help''': '''Log filename used if print statements are saved in log.'''}   ,     )
			__lowerCamelCase	:       int							=    field(default=3   ,     metadata={'''help''': '''Times an experiment will be run.'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
			            ''' model weights.'''
			        )
			    }   ,     )






			def    a_       (			self   :    Dict   )	->	Union[str, Any]:







					"""simple docstring"""






					warnings.warn(
					    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
					    """ are deprecated in general and it is advised to use external Benchmarking libraries """
					    """ to benchmark Transformer models."""							,	__lowerCAmelCase							,	)






			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					return json.dumps(dataclasses.asdict(self   )							,	indent=2   )






			@property
			def    a_       (			self   :    Tuple   )	->	List[str]:







					"""simple docstring"""






					if len(self.models   ) <= 0:
							raise ValueError(
							    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
							    """ bert-base-cased` or `args.models = ['bert-base-cased']."""   )
					return self.models







			@property
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[Any]:







					"""simple docstring"""






					if not self.multi_process:
							return False
					elif self.is_tpu:
							logger.info("""Multiprocessing is currently not possible on TPU."""   )
							return False
					else:
							return True



