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__						      =			logging.getLogger(__name__)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''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    __magic_name__(	)    ->     Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase           =					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'''			,    )
      _lowerCamelCase           =					parser.parse_args()

      _lowerCamelCase           =					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''		)
      _lowerCamelCase           =					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
      _lowerCamelCase           =					AutoTokenizer.from_pretrained(args.model_name_or_path		)
      _lowerCamelCase           =					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		)
      _lowerCamelCase           =					args.prompt if args.prompt else input('''Model prompt >>> '''		)

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

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

      _lowerCamelCase           =					[]
      for generated_sequence_idx, generated_sequence in enumerate(__UpperCAmelCase		):
            print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='		)
            _lowerCamelCase           =					generated_sequence.tolist()

            # Decode text
            _lowerCamelCase           =					tokenizer.decode(__UpperCAmelCase			,    clean_up_tokenization_spaces=__UpperCAmelCase			,    add_special_tokens=__UpperCAmelCase		)

            # Remove all text after the stop token
            _lowerCamelCase           =					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
            _lowerCamelCase           =					(
                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()
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)