


import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


snake_case_  :	Any							  =							{
    '''gpt-j-6b''': {'''is_causal''': True, '''model''': '''sgugger/sharded-gpt-j-6B''', '''tokenizer''': '''EleutherAI/gpt-j-6B'''},
    '''gpt-neox''': {'''is_causal''': True, '''model''': '''EleutherAI/gpt-neox-20b'''},
    '''opt''': {'''is_causal''': True, '''model''': '''facebook/opt-30b'''},
    '''T0pp''': {'''is_causal''': False, '''model''': '''bigscience/T0pp''', '''model_revision''': '''sharded'''},
}

snake_case_  :	Union[str, Any]							  =							[
    '''Hello, my name is''',
    '''Are unicorns real? Unicorns are''',
    '''For the first time in several years,''',
    '''My name is Julien and I am''',
    '''The goal of life is''',
    '''Whenever I\'m sad, I like to''',
]







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.''')
				parser.add_argument('''model_name'''		,type=a__		,default=a__		,help='''The name of the model to try.''')
				parser.add_argument(
				    '''--tokenizer_name'''		,type=a__		,default=a__		,help='''The name of the tokenizer (if different from the model.''')
				parser.add_argument('''--is_causal'''		,type=a__		,default=a__		,help='''Whether or not the model is causal.''')
				parser.add_argument(
				    '''--model_revision'''		,type=a__		,default=a__		,help='''The revision to use for the model checkpoint.''')
				parser.add_argument('''--torch_dtype'''		,type=a__		,default=a__		,help='''The dtype for the model.''')
				parser.add_argument('''--disk_offload'''		,action='''store_true''')

				_SCREAMING_SNAKE_CASE	     =parser.parse_args()

				# Sanitize args
				if args.model_name in DEFAULT_MODELS:
								_SCREAMING_SNAKE_CASE	     =DEFAULT_MODELS[args.model_name]
								_SCREAMING_SNAKE_CASE	     =defaults['''model''']
								if args.tokenizer_name is None:
												_SCREAMING_SNAKE_CASE	     =defaults.get('''tokenizer'''		,args.model_name)
								if args.is_causal is None:
												_SCREAMING_SNAKE_CASE	     =defaults['''is_causal''']
								if args.model_revision is None:
												_SCREAMING_SNAKE_CASE	     =defaults.get('''model_revision'''		,'''main''')

				if args.is_causal is None:
								raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.''')
				if args.tokenizer_name is None:
								_SCREAMING_SNAKE_CASE	     =args.model_name
				if args.model_revision is None:
								_SCREAMING_SNAKE_CASE	     ='''main'''

				return args







def   lowerCamelCase(      ):
				transformers.utils.logging.set_verbosity_error()
				_SCREAMING_SNAKE_CASE	     =parse_args()

				if args.torch_dtype is None:
								_SCREAMING_SNAKE_CASE	     =AutoConfig.from_pretrained(args.model_name)
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,'''torch_dtype'''		,torch.floataa)
				else:
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,args.torch_dtype)
				_SCREAMING_SNAKE_CASE	     =AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
				_SCREAMING_SNAKE_CASE	     ={
				    '''torch_dtype''': torch_dtype,
				    '''revision''': args.model_revision,
				}
				if args.disk_offload:
								_SCREAMING_SNAKE_CASE	     ='''tmp_offload'''
								_SCREAMING_SNAKE_CASE	     =True

				_SCREAMING_SNAKE_CASE	     =start_measure()
				_SCREAMING_SNAKE_CASE	     =model_cls.from_pretrained(args.model_name		,device_map='''auto'''		,**a__)
				_SCREAMING_SNAKE_CASE	     =end_measure(a__)
				log_measures(a__		,'''Model loading''')

				_SCREAMING_SNAKE_CASE	     =compute_module_sizes(a__)
				_SCREAMING_SNAKE_CASE	     ={v: 0 for v in model.hf_device_map.values()}
				for module, device in model.hf_device_map.items():
								device_size[device] += module_sizes[module]
				_SCREAMING_SNAKE_CASE	     ='''\n'''.join([f"- {device}: {size // 2**20}MiB" for device, size in device_size.items()])
				print(f"\nTheoretical use:\n{message}")

				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(args.tokenizer_name)

				_SCREAMING_SNAKE_CASE	     =start_measure()
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =[]
				for prompt in PROMPTS:
								_SCREAMING_SNAKE_CASE	     =tokenizer(a__		,return_tensors='''pt''').to(0)
								_SCREAMING_SNAKE_CASE	     =inputs['''input_ids'''][0].tolist()
								_SCREAMING_SNAKE_CASE	     =time.time()
								_SCREAMING_SNAKE_CASE	     =model.generate(inputs['''input_ids'''])
								_SCREAMING_SNAKE_CASE	     =time.time()
								_SCREAMING_SNAKE_CASE	     =outputs[0].tolist()
								_SCREAMING_SNAKE_CASE	     =len(a__) if outputs[: len(a__)] != tokens else len(a__) - len(a__)
								_SCREAMING_SNAKE_CASE	     =after_generate - before_generate

								_SCREAMING_SNAKE_CASE	     =tokenizer.decode(a__		,skip_special_tokens=a__)
								texts_outs.append(a__)
								generation_times.append(a__)
								gen_tokens.append(a__)
								print(f"Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n")

				_SCREAMING_SNAKE_CASE	     =end_measure(a__)
				log_measures(a__		,'''Model generation''')

				_SCREAMING_SNAKE_CASE	     =[gen / tok for gen, tok in zip(a__		,a__)]
				_SCREAMING_SNAKE_CASE	     =sum(a__) / len(a__)
				print(f"Average time of generation per token: {avg_gen:.2f}s")
				print(f"First generation (avg time per token): {generation_times_per_token[0]:.2f}s")
				_SCREAMING_SNAKE_CASE	     =sum(generation_times_per_token[1:]) / (len(a__) - 1)
				print(f"Average time of generation per token (excluding the first): {avg_gen:.2f}s")


if __name__ == "__main__":
							main()



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =False if not self.vocab_file else True

										try:
														import jieba
										except ModuleNotFoundError as error:
														raise error.__class__(
														    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
														    '''See https://pypi.org/project/jieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[2]

										if token_ids_a is None:
														return len(token_ids_a + sep     ) * [0] + cls_segment_id
										return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




						def 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''     )

										if not os.path.isdir(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text