'''simple docstring'''


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__	:     Union[str, 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__	:     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       ( ):



     """simple docstring"""





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

     UpperCAmelCase_				:  Union[str, Any]  = parser.parse_args()

     # Sanitize args
     if args.model_name in DEFAULT_MODELS:
          UpperCAmelCase_				:  Dict  = DEFAULT_MODELS[args.model_name]
          UpperCAmelCase_				:  Tuple  = defaults['model']
          if args.tokenizer_name is None:
               UpperCAmelCase_				:  Dict  = defaults.get('tokenizer'      ,	args.model_name  )
          if args.is_causal is None:
               UpperCAmelCase_				:  Union[str, Any]  = defaults['is_causal']
          if args.model_revision is None:
               UpperCAmelCase_				:  Optional[Any]  = 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:
          UpperCAmelCase_				:  str  = args.model_name
     if args.model_revision is None:
          UpperCAmelCase_				:  Tuple  = 'main'

     return args


def    _lowerCamelCase       ( ):



     """simple docstring"""





     transformers.utils.logging.set_verbosity_error()
     UpperCAmelCase_				:  Optional[Any]  = parse_args()

     if args.torch_dtype is None:
          UpperCAmelCase_				:  Optional[Any]  = AutoConfig.from_pretrained(args.model_name  )
          UpperCAmelCase_				:  List[Any]  = getattr(lowerCamelCase_      ,	'torch_dtype'      ,	torch.floataa  )
     else:
          UpperCAmelCase_				:  Any  = getattr(lowerCamelCase_      ,	args.torch_dtype  )
     UpperCAmelCase_				:  Union[str, Any]  = AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
     UpperCAmelCase_				:  Any  = {
         'torch_dtype': torch_dtype,
         'revision': args.model_revision,
     }
     if args.disk_offload:
          UpperCAmelCase_				:  List[str]  = 'tmp_offload'
          UpperCAmelCase_				:  str  = True

     UpperCAmelCase_				:  Union[str, Any]  = start_measure()
     UpperCAmelCase_				:  Optional[int]  = model_cls.from_pretrained(args.model_name      ,	device_map='auto'      ,	**lowerCamelCase_  )
     UpperCAmelCase_				:  Any  = end_measure(lowerCamelCase_  )
     log_measures(lowerCamelCase_      ,	'Model loading'  )

     UpperCAmelCase_				:  int  = compute_module_sizes(lowerCamelCase_  )
     UpperCAmelCase_				:  Tuple  = {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]
     UpperCAmelCase_				:  Any  = '\n'.join([F'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]  )
     print(F'''\nTheoretical use:\n{message}'''  )

     UpperCAmelCase_				:  int  = AutoTokenizer.from_pretrained(args.tokenizer_name  )

     UpperCAmelCase_				:  List[str]  = start_measure()
     UpperCAmelCase_				:  Optional[Any]  = []
     UpperCAmelCase_				:  List[str]  = []
     UpperCAmelCase_				:  List[Any]  = []
     for prompt in PROMPTS:
          UpperCAmelCase_				:  Union[str, Any]  = tokenizer(lowerCamelCase_      ,	return_tensors='pt'  ).to(0  )
          UpperCAmelCase_				:  Any  = inputs['input_ids'][0].tolist()
          UpperCAmelCase_				:  Union[str, Any]  = time.time()
          UpperCAmelCase_				:  Dict  = model.generate(inputs['input_ids']  )
          UpperCAmelCase_				:  int  = time.time()
          UpperCAmelCase_				:  Optional[int]  = outputs[0].tolist()
          UpperCAmelCase_				:  Any  = len(lowerCamelCase_  ) if outputs[: len(lowerCamelCase_  )] != tokens else len(lowerCamelCase_  ) - len(lowerCamelCase_  )
          UpperCAmelCase_				:  List[str]  = after_generate - before_generate

          UpperCAmelCase_				:  Tuple  = tokenizer.decode(lowerCamelCase_      ,	skip_special_tokens=lowerCamelCase_  )
          texts_outs.append(lowerCamelCase_  )
          generation_times.append(lowerCamelCase_  )
          gen_tokens.append(lowerCamelCase_  )
          print(F'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''  )

     UpperCAmelCase_				:  List[str]  = end_measure(lowerCamelCase_  )
     log_measures(lowerCamelCase_      ,	'Model generation'  )

     UpperCAmelCase_				:  Any  = [gen / tok for gen, tok in zip(lowerCamelCase_      ,	lowerCamelCase_  )]
     UpperCAmelCase_				:  int  = sum(lowerCamelCase_  ) / len(lowerCamelCase_  )
     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'''  )
     UpperCAmelCase_				:  int  = sum(generation_times_per_token[1:]  ) / (len(lowerCamelCase_  ) - 1)
     print(F'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''  )


if __name__ == "__main__":
 main()








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case					:							List[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

									with patch.object(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





