




'''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_				:     str     			=						logging.getLogger(__name__)







def 	_UpperCamelCase       (_lowerCamelCase							:   Dict		)->      List[str]:




     '''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 	_UpperCamelCase       ()->      Any:




     '''simple docstring'''





     __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()










# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

										# set step values
										self.scheduler.set_timesteps(_a     )

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

										_SCREAMING_SNAKE_CASE	     =(image / 2 + 0.5).clamp(0 ,			1     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     ).numpy()
										if output_type == "pil":
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pil(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"