






"""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					=     logging.getLogger(__name__)






def 							A(    snake_case_						):
       """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(    ):
       """simple docstring"""





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

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

       parser.add_argument("--seed"		,  type=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"		,  )
       lowercase__:							Optional[Any]         =     parser.parse_args()

       lowercase__:							List[Any]         =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
       lowercase__:							Union[str, Any]         =     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(snake_case_						)

       # Initialize the model and tokenizer
       lowercase__:							Optional[int]         =     AutoTokenizer.from_pretrained(args.model_name_or_path						)
       lowercase__:							str         =     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(snake_case_						)
       lowercase__:							Union[str, Any]         =     args.prompt if args.prompt else input("Model prompt >>> "						)

       lowercase__:							Optional[int]         =     tokenizer(snake_case_		,  return_tensors="pt"		,  add_special_tokens=snake_case_						)
       lowercase__:							Optional[Any]         =     {key: value.to(args.device						) for key, value in inputs.items()}

       lowercase__:							Optional[int]         =     model.generate(
           **snake_case_		,  max_length=args.length + len(inputs["input_ids"][0]						)		,  penalty_alpha=args.penalty_alpha		,  top_k=args.k		,  )

       lowercase__:							Tuple         =     []
       for generated_sequence_idx, generated_sequence in enumerate(snake_case_						):
              print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
              lowercase__:							Any         =     generated_sequence.tolist()

              # Decode text
              lowercase__:							Tuple         =     tokenizer.decode(snake_case_		,  clean_up_tokenization_spaces=snake_case_		,  add_special_tokens=snake_case_						)

              # Remove all text after the stop token
              lowercase__:							Any         =     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
              lowercase__:							Optional[Any]         =     (
                  prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]		,  clean_up_tokenization_spaces=snake_case_						)						) :]
              )

              generated_sequences.append(snake_case_						)
              print(snake_case_						)

       return generated_sequences


if __name__ == "__main__":
  main()














"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""LEDTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






