


import unittest

from diffusers import __version__
from diffusers.utils import deprecate






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						Any    =    ".".join([str(int(__version__.split("."						)[0]						) + 1						)] + __version__.split("."						)[1:]						)
    UpperCAmelCase__		:						Optional[Any]    =    "0.0.1"






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				={'deprecated_arg': 4}

         with self.assertWarns(A_       ) as warning:
              __UpperCamelCase       				=deprecate('deprecated_arg'   ,  self.higher_version   ,  'message'   ,  take_from=A_       )

         assert output == 4
         assert (
             str(warning.warning       )
             == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
             " message"
         )






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				={'deprecated_arg': 4}

         with self.assertWarns(A_       ) as warning:
              __UpperCamelCase       				=deprecate(('deprecated_arg', self.higher_version, 'message')   ,  take_from=A_       )

         assert output == 4
         assert (
             str(warning.warning       )
             == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
             " message"
         )






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				={'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
         with self.assertWarns(A_       ) as warning:
              __UpperCamelCase				, __UpperCamelCase       				=deprecate(
                  ('deprecated_arg_1', self.higher_version, 'Hey')   ,  ('deprecated_arg_2', self.higher_version, 'Hey')   ,  take_from=A_   ,  )
         assert output_a == 4
         assert output_a == 8
         assert (
             str(warning.warnings[0].message       )
             == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
             f' {self.higher_version}. Hey'
         )
         assert (
             str(warning.warnings[1].message       )
             == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
             f' {self.higher_version}. Hey'
         )






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				={'deprecated_arg': 4}

         with self.assertRaises(A_       ) as error:
              deprecate(('wrong_arg', self.higher_version, 'message')   ,  take_from=A_       )

         assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
         assert "line" in str(error.exception       )
         assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )






    def _a   (     self       )    ->			Union[str, Any]:
         with self.assertWarns(A_       ) as warning:
              deprecate(('deprecated_arg', self.higher_version, 'message')       )

         assert (
             str(warning.warning       )
             == f'`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'
         )






    def _a   (     self       )    ->			Any:
         with self.assertWarns(A_       ) as warning:
              deprecate(
                  ('deprecated_arg_1', self.higher_version, 'Hey')   ,  ('deprecated_arg_2', self.higher_version, 'Hey')   ,  )
         assert (
             str(warning.warnings[0].message       )
             == f'`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'
         )
         assert (
             str(warning.warnings[1].message       )
             == f'`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'
         )






    def _a   (     self       )    ->			Any:






         class        UpperCAmelCase__			:



             """simple docstring"""






             UpperCAmelCase__		:						List[str]    =    5

         with self.assertWarns(A_       ) as warning:
              __UpperCamelCase       				=deprecate(('arg', self.higher_version, 'message')   ,  take_from=Args()       )

         assert arg == 5
         assert (
             str(warning.warning       )
             == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
         )






    def _a   (     self       )    ->			Tuple:






         class        UpperCAmelCase__			:



             """simple docstring"""






             UpperCAmelCase__		:						int    =    5
             UpperCAmelCase__		:						Optional[int]    =    7

         with self.assertWarns(A_       ) as warning:
              __UpperCamelCase				, __UpperCamelCase       				=deprecate(
                  ('arg', self.higher_version, 'message')   ,  ('foo', self.higher_version, 'message')   ,  ('does not exist', self.higher_version, 'message')   ,  take_from=Args()   ,  )

         assert arg_a == 5
         assert arg_a == 7
         assert (
             str(warning.warning       )
             == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
         )
         assert (
             str(warning.warnings[0].message       )
             == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
         )
         assert (
             str(warning.warnings[1].message       )
             == f'The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'
         )






    def _a   (     self       )    ->			Union[str, Any]:
         __UpperCamelCase       				={'deprecated_arg': 4}

         with self.assertRaises(A_       ) as error:
              deprecate(('wrong_arg', self.lower_version, 'message')   ,  take_from=A_       )

         assert (
             str(error.exception       )
             == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
             f' {__version__} is >= {self.lower_version}'
         )






    def _a   (     self       )    ->			Optional[Any]:
         with self.assertWarns(A_       ) as warning:
              deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')   ,  standard_warn=A_       )

         assert str(warning.warning       ) == "This message is better!!!"






    def _a   (     self       )    ->			List[Any]:
         with self.assertWarns(A_       ) as warning:
              deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')   ,  standard_warn=A_       )
         assert str(warning.warning       ) == "This message is better!!!"
         assert "diffusers/tests/others/test_utils.py" in warning.filename









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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=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'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=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
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=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__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     __UpperCamelCase       				=tokenizer(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				={key: value.to(args.device							) for key, value in inputs.items()}

     __UpperCamelCase       				=model.generate(
         **SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(inputs['input_ids'][0]							)     ,	penalty_alpha=args.penalty_alpha     ,	top_k=args.k     ,	)

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

          # Decode text
          __UpperCamelCase       				=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
          __UpperCamelCase       				=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       				=(
              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()





