




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,
)
_lowerCamelCase							=      logging.getLogger(__name__)





def 							lowerCamelCase    (							UpperCAmelCase_	:      str      )->			List[str]:

       """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 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




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

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

       parser.add_argument("""--seed"""   ,       type=UpperCAmelCase_   ,       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"""   ,       )
       a			   =parser.parse_args()

       a			   =torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
       a			   =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(UpperCAmelCase_      )

       # Initialize the model and tokenizer
       a			   =AutoTokenizer.from_pretrained(args.model_name_or_path      )
       a			   =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(UpperCAmelCase_      )
       a			   =args.prompt if args.prompt else input("""Model prompt >>> """      )

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

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

       a			   =[]
       for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase_      ):
              print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
              a			   =generated_sequence.tolist()

              # Decode text
              a			   =tokenizer.decode(UpperCAmelCase_   ,       clean_up_tokenization_spaces=UpperCAmelCase_   ,       add_special_tokens=UpperCAmelCase_      )

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

              generated_sequences.append(UpperCAmelCase_      )
              print(UpperCAmelCase_      )

       return generated_sequences


if __name__ == "__main__":
    main()






from typing import TYPE_CHECKING

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


_lowerCamelCase							=      {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CvtForImageClassification''',
        '''CvtModel''',
        '''CvtPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFCvtForImageClassification''',
        '''TFCvtModel''',
        '''TFCvtPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cvt import (
            CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CvtForImageClassification,
            CvtModel,
            CvtPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_cvt import (
            TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCvtForImageClassification,
            TFCvtModel,
            TFCvtPreTrainedModel,
        )


else:
    import sys

    _lowerCamelCase							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
