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       a__      (					a							)		->	Union[str, Any]:
       np.random.seed(args.seed							)
       torch.manual_seed(args.seed							)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed							)


def       a__      (					)		->	str:
       A_							:       List[str]	      =				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=2_0							)
       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=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'''  ,   )
       A_							:       List[Any]	      =				parser.parse_args()

       A_							:       Optional[Any]	      =				torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
       A_							:       Optional[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(_a							)

       # Initialize the model and tokenizer
       A_							:       Optional[int]	      =				AutoTokenizer.from_pretrained(args.model_name_or_path							)
       A_							:       Any	      =				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							)
       A_							:       List[Any]	      =				args.prompt if args.prompt else input('''Model prompt >>> '''							)

       A_							:       Dict	      =				tokenizer(_a  ,   return_tensors='''pt'''  ,   add_special_tokens=_a							)
       A_							:       Union[str, Any]	      =				{key: value.to(args.device							) for key, value in inputs.items()}

       A_							:       Tuple	      =				model.generate(
           **_a  ,   max_length=args.length + len(inputs['''input_ids'''][0]							)  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

       A_							:       Union[str, Any]	      =				[]
       for generated_sequence_idx, generated_sequence in enumerate(_a							):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""							)
              A_							:       str	      =				generated_sequence.tolist()

              # Decode text
              A_							:       int	      =				tokenizer.decode(_a  ,   clean_up_tokenization_spaces=_a  ,   add_special_tokens=_a							)

              # Remove all text after the stop token
              A_							:       Any	      =				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_							:       Union[str, Any]	      =				(
                  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'''





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]=sys.maxsize     ) ->							Tuple:
        snake_case_    							=				"bilinear"
        snake_case_    							=				max_size
        snake_case_    							=				short_edge_length


  def __call__(self	:			int						,			lowerCAmelCase_	:			Tuple     ) ->							int:
        snake_case_    							=				[]
        for img in imgs:
              snake_case_							,						snake_case_    							=				img.shape[:2]
              # later: provide list and randomly choose index for resize
              snake_case_    							=				np.random.randint(self.short_edge_length[0]						,			self.short_edge_length[1] + 1     )
              if size == 0:
                    return img
              snake_case_    							=				size * 1.0 / min(lowerCAmelCase_						,			lowerCAmelCase_     )
              if h < w:
                    snake_case_							,						snake_case_    							=				size, scale * w
              else:
                    snake_case_							,						snake_case_    							=				scale * h, size
              if max(lowerCAmelCase_						,			lowerCAmelCase_     ) > self.max_size:
                    snake_case_    							=				self.max_size * 1.0 / max(lowerCAmelCase_						,			lowerCAmelCase_     )
                    snake_case_    							=				newh * scale
                    snake_case_    							=				neww * scale
              snake_case_    							=				int(neww + 0.5     )
              snake_case_    							=				int(newh + 0.5     )

              if img.dtype == np.uinta:
                    snake_case_    							=				Image.fromarray(lowerCAmelCase_     )
                    snake_case_    							=				pil_image.resize((neww, newh)						,			PILImageResampling.BILINEAR     )
                    snake_case_    							=				np.asarray(lowerCAmelCase_     )
              else:
                    snake_case_    							=				img.permute(2						,			0						,			1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
                    snake_case_    							=				nn.functional.interpolate(
                        lowerCAmelCase_						,			(newh, neww)						,			mode=self.interp_method						,			align_corners=lowerCAmelCase_     ).squeeze(0     )
              img_augs.append(lowerCAmelCase_     )

        return img_augs



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]     ) ->							int:
        snake_case_    							=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,			cfg.INPUT.MAX_SIZE_TEST     )
        snake_case_    							=				cfg.INPUT.FORMAT
        snake_case_    							=				cfg.SIZE_DIVISIBILITY
        snake_case_    							=				cfg.PAD_VALUE
        snake_case_    							=				cfg.INPUT.MAX_SIZE_TEST
        snake_case_    							=				cfg.MODEL.DEVICE
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_STD     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				torch.tensor(cfg.MODEL.PIXEL_MEAN     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,			1						,			1     )
        snake_case_    							=				lambda lowerCAmelCase_     : (x - self.pixel_mean) / self.pixel_std

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple     ) ->							Any:
        snake_case_    							=				tuple(max(lowerCAmelCase_     ) for s in zip(*[img.shape for img in images]     )     )
        snake_case_    							=				[im.shape[-2:] for im in images]
        snake_case_    							=				[
            nn.functional.pad(
                lowerCAmelCase_						,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,			value=self.pad_value						,			)
            for size, im in zip(lowerCAmelCase_						,			lowerCAmelCase_     )
        ]

        return torch.stack(lowerCAmelCase_     ), torch.tensor(lowerCAmelCase_     )


  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]=False     ) ->							int:
        with torch.no_grad():
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				[images]
              if single_image:
                    assert len(lowerCAmelCase_     ) == 1
              for i in range(len(lowerCAmelCase_     )     ):
                    if isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(lowerCAmelCase_						,			images.pop(lowerCAmelCase_     ).to(self.device     ).float()     )
                    elif not isinstance(images[i]						,			torch.Tensor     ):
                          images.insert(
                              lowerCAmelCase_						,			torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_     )						,			input_format=self.input_format     )     )
                              .to(self.device     )
                              .float()						,			)
            # resize smallest edge
              snake_case_    							=				torch.tensor([im.shape[:2] for im in images]     )
              snake_case_    							=				self.aug(lowerCAmelCase_     )
              # transpose images and convert to torch tensors
              # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
              # now normalize before pad to avoid useless arithmetic
              snake_case_    							=				[self.normalizer(lowerCAmelCase_     ) for x in images]
              # now pad them to do the following operations
              snake_case_							,						snake_case_    							=				self.pad(lowerCAmelCase_     )
              # Normalize

              if self.size_divisibility > 0:
                    raise NotImplementedError()
              # pad
              snake_case_    							=				torch.true_divide(lowerCAmelCase_						,			lowerCAmelCase_     )
              if single_image:
                    return images[0], sizes[0], scales_yx[0]
              else:
                    return images, sizes, scales_yx

def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]      ):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes




def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Tuple[int, int]      ):
      assert torch.isfinite(_a      ).all(), "Box tensor contains infinite or NaN!"
      snake_case_							,						snake_case_    							=				box_size
      tensor[:, 0].clamp_(min=0			,       max=_a      )
      tensor[:, 1].clamp_(min=0			,       max=_a      )
      tensor[:, 2].clamp_(min=0			,       max=_a      )
      tensor[:, 3].clamp_(min=0			,       max=_a      )

