



"""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,
)
__snake_case	:				List[str]  					=			logging.getLogger(__name__)



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



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

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

     parser.add_argument('''--seed'''		,							type=__a		,							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'''		,							)
     A__				=   parser.parse_args()

     A__				=   torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
     A__				=   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(__a      )

     # Initialize the model and tokenizer
     A__				=   AutoTokenizer.from_pretrained(args.model_name_or_path      )
     A__				=   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(__a      )
     A__				=   args.prompt if args.prompt else input('''Model prompt >>> '''      )

     A__				=   tokenizer(__a		,							return_tensors='''pt'''		,							add_special_tokens=__a      )
     A__				=   {key: value.to(args.device      ) for key, value in inputs.items()}

     A__				=   model.generate(
         **__a		,							max_length=args.length + len(inputs['''input_ids'''][0]      )		,							penalty_alpha=args.penalty_alpha		,							top_k=args.k		,							)

     A__				=   []
     for generated_sequence_idx, generated_sequence in enumerate(__a      ):
          print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
          A__				=   generated_sequence.tolist()

          # Decode text
          A__				=   tokenizer.decode(__a		,							clean_up_tokenization_spaces=__a		,							add_special_tokens=__a      )

          # Remove all text after the stop token
          A__				=   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
          A__				=   (
              prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]		,							clean_up_tokenization_spaces=__a      )      ) :]
          )

          generated_sequences.append(__a      )
          print(__a      )

     return generated_sequences


if __name__ == "__main__":
 main()








"""simple docstring"""







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[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   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(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



