
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		:   Dict         =    logging.getLogger(__name__)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    Any:
						np.random.seed(args.seed					)
						torch.manual_seed(args.seed					)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed					)




def   UpperCamelCase__     (    )     ->    str:
						_lowercase									=				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"""		,      )
						_lowercase									=				parser.parse_args()

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

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

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

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

												# Decode text
												_lowercase									=				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
												_lowercase									=				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									=				(
												    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()

import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A		:   Optional[int]         =    TypeVar('''T''', bound='''TaskTemplate''')



@dataclass(frozen=_a						)
class       a_   ( abc.ABC						):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			a  : str
			a  : ClassVar[Features]
			a  : ClassVar[Features]






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									return copy.deepcopy(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return Features(**self.input_schema							, **self.label_schema     )






			@property
			@abc.abstractmethod
			def       UpperCamelCase_							(			self     ):
									raise NotImplementedError






			@classmethod
			def       UpperCamelCase_							(			cls							, __UpperCamelCase     ):
									_lowercase									=				{f.name for f in dataclasses.fields(cls     )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )