
'''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 	snake_case_							(				_a  :				Any      ):
      np.random.seed(args.seed      )
      torch.manual_seed(args.seed      )
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed      )
def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser()
      parser.add_argument(
          "--model_name_or_path"			,       default=_a			,       type=_a			,       required=_a			,       )
      parser.add_argument("--prompt"			,       type=_a			,       default=""      )
      parser.add_argument("--length"			,       type=_a			,       default=20      )
      parser.add_argument("--stop_token"			,       type=_a			,       default=_a			,       help="Token at which text generation is stopped"      )
      parser.add_argument(
          "--temperature"			,       type=_a			,       default=1.0			,       help="temperature of 1.0 has no effect, lower tend toward greedy sampling"			,       )
      parser.add_argument(
          "--repetition_penalty"			,       type=_a			,       default=1.0			,       help="primarily useful for CTRL model; in that case, use 1.2"      )
      parser.add_argument("--k"			,       type=_a			,       default=0      )
      parser.add_argument("--penalty_alpha"			,       type=_a			,       default=0.0      )
      parser.add_argument("--p"			,       type=_a			,       default=0.9      )

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

      parser.add_argument("--seed"			,       type=_a			,       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"			,       )
      snake_case_    							=				parser.parse_args()

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

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

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

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

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

            # Decode text
            snake_case_    							=				tokenizer.decode(_a			,       clean_up_tokenization_spaces=_a			,       add_special_tokens=_a      )

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

            generated_sequences.append(_a      )
            print(_a      )

      return generated_sequences


if __name__ == "__main__":
     main()



'''simple docstring'''





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline

