






'''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,
)
UpperCamelCase_					=				logging.getLogger(__name__)






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






def  lowerCAmelCase__		(					)   -> Any:
       UpperCAmelCase__	:   Optional[int]												=	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=2_0			)
       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=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'''			,						)
       UpperCAmelCase__	:   int												=	parser.parse_args()

       UpperCAmelCase__	:   List[str]												=	torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
       UpperCAmelCase__	:   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(__snake_case			)

       # Initialize the model and tokenizer
       UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(args.model_name_or_path			)
       UpperCAmelCase__	:   int												=	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			)
       UpperCAmelCase__	:   Union[str, Any]												=	args.prompt if args.prompt else input('''Model prompt >>> '''			)

       UpperCAmelCase__	:   str												=	tokenizer(__snake_case			,						return_tensors='''pt'''			,						add_special_tokens=__snake_case			)
       UpperCAmelCase__	:   Optional[Any]												=	{key: value.to(args.device			) for key, value in inputs.items()}

       UpperCAmelCase__	:   str												=	model.generate(
           **__snake_case			,						max_length=args.length + len(inputs['''input_ids'''][0]			)			,						penalty_alpha=args.penalty_alpha			,						top_k=args.k			,						)

       UpperCAmelCase__	:   int												=	[]
       for generated_sequence_idx, generated_sequence in enumerate(__snake_case			):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""			)
              UpperCAmelCase__	:   Tuple												=	generated_sequence.tolist()

              # Decode text
              UpperCAmelCase__	:   Union[str, Any]												=	tokenizer.decode(__snake_case			,						clean_up_tokenization_spaces=__snake_case			,						add_special_tokens=__snake_case			)

              # Remove all text after the stop token
              UpperCAmelCase__	:   Optional[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
              UpperCAmelCase__	:   str												=	(
                  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()




def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[int]:






      '''simple docstring'''







      if hue < 0 or hue > 3_6_0:
            raise Exception("hue should be between 0 and 360"     )

      if saturation < 0 or saturation > 1:
            raise Exception("saturation should be between 0 and 1"     )

      if value < 0 or value > 1:
            raise Exception("value should be between 0 and 1"     )

      UpperCAmelCase_	:						Tuple					   =		value * saturation
      UpperCAmelCase_	:						Union[str, Any]					   =		hue / 6_0
      UpperCAmelCase_	:						Tuple					   =		chroma * (1 - abs(hue_section % 2 - 1     ))
      UpperCAmelCase_	:						Tuple					   =		value - chroma

      if hue_section >= 0 and hue_section <= 1:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 1 and hue_section <= 2:
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 2 and hue_section <= 3:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )
      elif hue_section > 3 and hue_section <= 4:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Optional[Any]					   =		round(2_5_5 * (chroma + match_value)     )
      elif hue_section > 4 and hue_section <= 5:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (chroma + match_value)     )
      else:
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )

      return [red, green, blue]







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[float]:






      '''simple docstring'''







      if red < 0 or red > 2_5_5:
            raise Exception("red should be between 0 and 255"     )

      if green < 0 or green > 2_5_5:
            raise Exception("green should be between 0 and 255"     )

      if blue < 0 or blue > 2_5_5:
            raise Exception("blue should be between 0 and 255"     )

      UpperCAmelCase_	:						str					   =		red / 2_5_5
      UpperCAmelCase_	:						Dict					   =		green / 2_5_5
      UpperCAmelCase_	:						int					   =		blue / 2_5_5
      UpperCAmelCase_	:						Any					   =		max(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Union[str, Any]					   =		value - min(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Optional[Any]					   =		0 if value == 0 else chroma / value

      if chroma == 0:
            UpperCAmelCase_	:						Tuple					   =		0.0
      elif value == float_red:
            UpperCAmelCase_	:						List[str]					   =		6_0 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
            UpperCAmelCase_	:						Union[str, Any]					   =		6_0 * (2 + (float_blue - float_red) / chroma)
      else:
            UpperCAmelCase_	:						Dict					   =		6_0 * (4 + (float_red - float_green) / chroma)

      UpperCAmelCase_	:						int					   =		(hue + 3_6_0) % 3_6_0

      return [hue, saturation, value]







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  bool:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		abs(hsv_a[0] - hsv_a[0]     ) < 0.2
      UpperCAmelCase_	:						Optional[Any]					   =		abs(hsv_a[1] - hsv_a[1]     ) < 0.002
      UpperCAmelCase_	:						Tuple					   =		abs(hsv_a[2] - hsv_a[2]     ) < 0.002

      return check_hue and check_saturation and check_value
