

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 				__a							(       __A						):
    np.random.seed(args.seed						)
    torch.manual_seed(args.seed						)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed						)





def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    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"			,							)
    SCREAMING_SNAKE_CASE__         =    parser.parse_args()

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

    SCREAMING_SNAKE_CASE__         =    tokenizer(_snake_case			,							return_tensors="pt"			,							add_special_tokens=_snake_case						)
    SCREAMING_SNAKE_CASE__         =    {key: value.to(args.device						) for key, value in inputs.items()}

    SCREAMING_SNAKE_CASE__         =    model.generate(
        **_snake_case			,							max_length=args.length + len(inputs["input_ids"][0]						)			,							penalty_alpha=args.penalty_alpha			,							top_k=args.k			,							)

    SCREAMING_SNAKE_CASE__         =    []
    for generated_sequence_idx, generated_sequence in enumerate(_snake_case						):
        print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="						)
        SCREAMING_SNAKE_CASE__         =    generated_sequence.tolist()

        # Decode text
        SCREAMING_SNAKE_CASE__         =    tokenizer.decode(_snake_case			,							clean_up_tokenization_spaces=_snake_case			,							add_special_tokens=_snake_case						)

        # Remove all text after the stop token
        SCREAMING_SNAKE_CASE__         =    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
        SCREAMING_SNAKE_CASE__         =    (
            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()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    ={
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """SplinterForQuestionAnswering""",
		    """SplinterForPreTraining""",
		    """SplinterLayer""",
		    """SplinterModel""",
		    """SplinterPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

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



