




'''simple docstring'''





import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
UpperCamelCase_		:    int									=		logging.getLogger(__name__)





def 			__a			(     _UpperCamelCase:					Optional[Any]					)		->				Any:

     """simple docstring"""




     np.random.seed(args.seed					)
     torch.manual_seed(args.seed					)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   argparse.ArgumentParser()
     parser.add_argument(
         "--model_name_or_path"      ,		default=_UpperCamelCase      ,		type=_UpperCamelCase      ,		required=_UpperCamelCase      ,		)
     parser.add_argument("--prompt"      ,		type=_UpperCamelCase      ,		default=""					)
     parser.add_argument("--length"      ,		type=_UpperCamelCase      ,		default=20					)
     parser.add_argument("--stop_token"      ,		type=_UpperCamelCase      ,		default=_UpperCamelCase      ,		help="Token at which text generation is stopped"					)
     parser.add_argument(
         "--temperature"      ,		type=_UpperCamelCase      ,		default=1.0      ,		help="temperature of 1.0 has no effect, lower tend toward greedy sampling"      ,		)
     parser.add_argument(
         "--repetition_penalty"      ,		type=_UpperCamelCase      ,		default=1.0      ,		help="primarily useful for CTRL model; in that case, use 1.2"					)
     parser.add_argument("--k"      ,		type=_UpperCamelCase      ,		default=0					)
     parser.add_argument("--penalty_alpha"      ,		type=_UpperCamelCase      ,		default=0.0					)
     parser.add_argument("--p"      ,		type=_UpperCamelCase      ,		default=0.9					)

     parser.add_argument("--prefix"      ,		type=_UpperCamelCase      ,		default=""      ,		help="Text added prior to input."					)
     parser.add_argument("--padding_text"      ,		type=_UpperCamelCase      ,		default=""      ,		help="Deprecated, the use of `--prefix` is preferred."					)
     parser.add_argument("--xlm_language"      ,		type=_UpperCamelCase      ,		default=""      ,		help="Optional language when used with the XLM model."					)

     parser.add_argument("--seed"      ,		type=_UpperCamelCase      ,		default=42      ,		help="random seed for initialization"					)
     parser.add_argument("--no_cuda"      ,		action="store_true"      ,		help="Avoid using CUDA when available"					)
     parser.add_argument(
         "--fp16"      ,		action="store_true"      ,		help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"      ,		)
     _snake_case							=   parser.parse_args()

     _snake_case							=   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"					)
     _snake_case							=   0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""					)

     set_seed(_UpperCamelCase					)

     # Initialize the model and tokenizer
     _snake_case							=   AutoTokenizer.from_pretrained(args.model_name_or_path					)
     _snake_case							=   AutoModelForCausalLM.from_pretrained(args.model_name_or_path					)

     # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
     # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
     model.to(args.device					)

     if args.fpaa:
          model.half()

     logger.info(_UpperCamelCase					)
     _snake_case							=   args.prompt if args.prompt else input("Model prompt >>> "					)

     _snake_case							=   tokenizer(_UpperCamelCase      ,		return_tensors="pt"      ,		add_special_tokens=_UpperCamelCase					)
     _snake_case							=   {key: value.to(args.device					) for key, value in inputs.items()}

     _snake_case							=   model.generate(
         **_UpperCamelCase      ,		max_length=args.length + len(inputs["input_ids"][0]					)      ,		penalty_alpha=args.penalty_alpha      ,		top_k=args.k      ,		)

     _snake_case							=   []
     for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase					):
          print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""					)
          _snake_case							=   generated_sequence.tolist()

          # Decode text
          _snake_case							=   tokenizer.decode(_UpperCamelCase      ,		clean_up_tokenization_spaces=_UpperCamelCase      ,		add_special_tokens=_UpperCamelCase					)

          # Remove all text after the stop token
          _snake_case							=   text[: text.find(args.stop_token					) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          _snake_case							=   (
              prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]      ,		clean_up_tokenization_spaces=_UpperCamelCase					)					) :]
          )

          generated_sequences.append(_UpperCamelCase					)
          print(_UpperCamelCase					)

     return generated_sequences


if __name__ == "__main__":
   main()








'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_		:    List[str]									=		500000

UpperCamelCase_			,			UpperCamelCase_		:    Dict									=		os.path.split(__file__)
UpperCamelCase_		:    int									=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.select(range(0      ,		len(_UpperCamelCase					)      ,		2					)					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.sort("numbers"					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.shuffle()





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				Any:

     """simple docstring"""




     _snake_case							=   dataset.train_test_split(0.1					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset      ,		_UpperCamelCase:					int=10					)		->				List[str]:

     """simple docstring"""




     for shard_id in range(_UpperCamelCase					):
          _snake_case							=   dataset.shard(_UpperCamelCase      ,		_UpperCamelCase					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   {"num examples": SPEED_TEST_N_EXAMPLES}
     _snake_case							=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset"					)
          _snake_case							=   datasets.Features({"text": datasets.Value("string"					), "numbers": datasets.Value("float32"					)}					)
          _snake_case							=   generate_example_dataset(
              os.path.join(_UpperCamelCase      ,		"dataset.arrow"					)      ,		_UpperCamelCase      ,		num_examples=_UpperCamelCase					)
          print("Functions"					)
          for func in functions:
               print(func.__name__					)
               _snake_case							=   func(_UpperCamelCase					)

     with open(_UpperCamelCase      ,		"wb"					) as f:
          f.write(json.dumps(_UpperCamelCase					).encode("utf-8"					)					)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()


