
'''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_           =    logging.getLogger(__name__)

def     UpperCamelCase_  (						__SCREAMING_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     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    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=2_0							)
    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=4_2,		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",		)
    snake_case_			:     int        =    parser.parse_args()

    snake_case_			:     int        =    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"							)
    snake_case_			:     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(__SCREAMING_SNAKE_CASE							)

    # Initialize the model and tokenizer
    snake_case_			:     Any        =    AutoTokenizer.from_pretrained(args.model_name_or_path							)
    snake_case_			:     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(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Dict        =    args.prompt if args.prompt else input("Model prompt >>> "							)

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

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

    snake_case_			:     int        =    []
    for generated_sequence_idx, generated_sequence in enumerate(__SCREAMING_SNAKE_CASE							):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
        snake_case_			:     Any        =    generated_sequence.tolist()

        # Decode text
        snake_case_			:     Optional[int]        =    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
        snake_case_			:     Optional[int]        =    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
        snake_case_			:     List[str]        =    (
            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()






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_           =    {
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SamModel",
      "SamPreTrainedModel",
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSamModel",
      "TFSamPreTrainedModel",
  ]
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    ["SamImageProcessor"]


if TYPE_CHECKING:
  from .configuration_sam import (
      SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SamConfig,
      SamMaskDecoderConfig,
      SamPromptEncoderConfig,
      SamVisionConfig,
  )
  from .processing_sam import SamProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_sam import SamImageProcessor

else:
  import sys

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




