

'''simple docstring'''




import os

from ...utils import logging
from .utils import DataProcessor, InputExample


_a			:	Union[str, Any]								=  logging.get_logger(__name__)



class     lowercase_		(						_lowerCAmelCase	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=None       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		language
    UpperCAmelCase  							=		train_language
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		self.language if self.train_language is None else self.train_language
    UpperCAmelCase  							=		self._read_tsv(os.path.join(_lowerCamelCase				,	F'''XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv'''       )       )
    UpperCAmelCase  							=		[]
    for i, line in enumerate(_lowerCamelCase       ):
      if i == 0:
        continue
      UpperCAmelCase  							=		F'''train-{i}'''
      UpperCAmelCase  							=		line[0]
      UpperCAmelCase  							=		line[1]
      UpperCAmelCase  							=		'contradiction' if line[2] == 'contradictory' else line[2]
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training input {text_a} is not a string'''       )
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training input {text_b} is not a string'''       )
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training label {label} is not a string'''       )
      examples.append(InputExample(guid=_lowerCamelCase				,	text_a=_lowerCamelCase				,	text_b=_lowerCamelCase				,	label=_lowerCamelCase       )       )
    return examples
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		self._read_tsv(os.path.join(_lowerCamelCase				,	'XNLI-1.0/xnli.test.tsv'       )       )
    UpperCAmelCase  							=		[]
    for i, line in enumerate(_lowerCamelCase       ):
      if i == 0:
        continue
      UpperCAmelCase  							=		line[0]
      if language != self.language:
        continue
      UpperCAmelCase  							=		F'''test-{i}'''
      UpperCAmelCase  							=		line[6]
      UpperCAmelCase  							=		line[7]
      UpperCAmelCase  							=		line[1]
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training input {text_a} is not a string'''       )
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training input {text_b} is not a string'''       )
      if not isinstance(_lowerCamelCase				,	_lowerCamelCase       ):
        raise ValueError(F'''Training label {label} is not a string'''       )
      examples.append(InputExample(guid=_lowerCamelCase				,	text_a=_lowerCamelCase				,	text_b=_lowerCamelCase				,	label=_lowerCamelCase       )       )
    return examples





  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    return ["contradiction", "entailment", "neutral"]




_a			:	Dict								=  {
    'xnli': XnliProcessor,
}

_a			:	Tuple								=  {
    'xnli': 'classification',
}

_a			:	Dict								=  {
    'xnli': 3,
}





'''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,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


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


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

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

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				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'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

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

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

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

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

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

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

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

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


