


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,
)
_SCREAMING_SNAKE_CASE :					Any			=	logging.getLogger(__name__)







def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]			)  ->   Tuple:





     '''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 						_lowercase       (     )  ->   Union[str, Any]:





     '''simple docstring'''





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

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

     parser.add_argument('''--seed'''		,type=__lowerCamelCase		,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'''		,)
     UpperCamelCase__							:  int								=     parser.parse_args()

     UpperCamelCase__							:  Union[str, Any]								=     torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
     UpperCamelCase__							:  Tuple								=     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(__lowerCamelCase			)

     # Initialize the model and tokenizer
     UpperCamelCase__							:  Dict								=     AutoTokenizer.from_pretrained(args.model_name_or_path			)
     UpperCamelCase__							:  str								=     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(__lowerCamelCase			)
     UpperCamelCase__							:  Optional[Any]								=     args.prompt if args.prompt else input('''Model prompt >>> '''			)

     UpperCamelCase__							:  int								=     tokenizer(__lowerCamelCase		,return_tensors='''pt'''		,add_special_tokens=__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     {key: value.to(args.device			) for key, value in inputs.items()}

     UpperCamelCase__							:  Optional[Any]								=     model.generate(
         **__lowerCamelCase		,max_length=args.length + len(inputs['''input_ids'''][0]			)		,penalty_alpha=args.penalty_alpha		,top_k=args.k		,)

     UpperCamelCase__							:  Optional[int]								=     []
     for generated_sequence_idx, generated_sequence in enumerate(__lowerCamelCase			):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='			)
          UpperCamelCase__							:  Optional[Any]								=     generated_sequence.tolist()

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

          # Remove all text after the stop token
          UpperCamelCase__							:  List[str]								=     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=__lowerCamelCase			)			) :]
          )

          generated_sequences.append(__lowerCamelCase			)
          print(__lowerCamelCase			)

     return generated_sequences


if __name__ == "__main__":
   main()








import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class  UpperCamelCase__ ( unittest.TestCase			):






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> int:
           return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''   )






      @cached_property
      def 	__lowercase(     self					:   List[Any]   )  -> Tuple:
           return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''   )






      def 	__lowercase(     self					:   List[str]   )  -> Optional[int]:
           UpperCamelCase__							:  Optional[Any]								=     self.tokenizer_ab
           UpperCamelCase__							:  str								=     ''' I am a small frog.'''
           UpperCamelCase__							:  Dict								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  List[str]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   int   )  -> Any:
           UpperCamelCase__							:  str								=     self.rust_tokenizer_ab
           UpperCamelCase__							:  Optional[int]								=     ''' I am a small frog.'''
           UpperCamelCase__							:  List[str]								=     tok([src_text],		padding=__lowerCamelCase,		truncation=__lowerCamelCase   )['''input_ids''']
           UpperCamelCase__							:  Optional[int]								=     tok.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase,		clean_up_tokenization_spaces=__lowerCamelCase   )[0]
           assert src_text == decoded






      def 	__lowercase(     self					:   Optional[int]   )  -> Optional[Any]:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Optional[int]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']   ).input_ids == [[55_02, 2], [55_02, 2]]




