

'''simple docstring'''




# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_a			:	Optional[Any]								=  logging.get_logger(__name__)


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset

  from transformers import Trainer



  class     lowercase_		(						__lowerCamelCase	):

    '''simple docstring'''





    def __init__(       self				,	a_ = 1_0_1       )							->			List[Any]:


      """simple docstring"""
      UpperCAmelCase  							=		length
    def __len__(       self       )							->			str:


      """simple docstring"""
      return self.length





    def __getitem__(       self				,	a_       )							->			int:


      """simple docstring"""
      return i




  class     lowercase_		:

    '''simple docstring'''





    def __call__(       self				,	a_       )							->			Optional[Any]:


      """simple docstring"""
      return {"input_ids": torch.tensor(_UpperCamelCase       ), "labels": torch.tensor(_UpperCamelCase       )}




  class     lowercase_		(						nn.Module	):

    '''simple docstring'''





    def __init__(       self       )							->			List[Any]:


      """simple docstring"""
      super().__init__()
      # Add some (unused) params otherwise DDP will complain.
      UpperCAmelCase  							=		nn.Linear(1_2_0				,	8_0       )





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


      """simple docstring"""





      if labels is not None:
        return torch.tensor(0.0				,	device=input_ids.device       ), input_ids
      else:
        return input_ids


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		HfArgumentParser((TrainingArguments,)  )
  sys.argv += ["--output_dir", "./examples"]
  UpperCAmelCase  							=		parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
      f'''tpu_num_cores: {training_args.tpu_num_cores}'''    ,				)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1001, 256, 15]:
    UpperCAmelCase  							=		DummyDataset(__UpperCamelCase  )

    def compute_metrics(SCREAMING_SNAKE_CASE :				Union[str, Any]  ) -> Dict:
      UpperCAmelCase  							=		list(range(len(__UpperCamelCase  )  )  )
      UpperCAmelCase  							=		p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    UpperCAmelCase  							=		Trainer(
        model=DummyModel()    ,				args=__UpperCamelCase    ,				data_collator=DummyDataCollator()    ,				eval_dataset=__UpperCamelCase    ,				compute_metrics=__UpperCamelCase    ,				)
    UpperCAmelCase  							=		trainer.evaluate()
    logger.info(__UpperCamelCase  )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase  )
      exit(1  )

    UpperCAmelCase  							=		trainer.predict(__UpperCamelCase  )
    logger.info(p.metrics  )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics  )
      exit(1  )

    UpperCAmelCase  							=		2

    UpperCAmelCase  							=		trainer.evaluate()
    logger.info(__UpperCamelCase  )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase  )
      exit(1  )

    UpperCAmelCase  							=		trainer.predict(__UpperCamelCase  )
    logger.info(p.metrics  )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics  )
      exit(1  )

    UpperCAmelCase  							=		None

  logger.info('🔥 All distributed tests successful'  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict  ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()





'''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()


