





'''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,
)
_lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)




def    __a (     UpperCAmelCase    )      ->Any:



	"""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    __a (     )      ->Optional[Any]:



	"""simple docstring"""







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

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

	parser.add_argument("""--seed"""       ,			type=UpperCAmelCase       ,			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"""       ,			)
	A							  =      parser.parse_args()

	A							  =      torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
	A							  =      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(UpperCAmelCase    )

	# Initialize the model and tokenizer
	A							  =      AutoTokenizer.from_pretrained(args.model_name_or_path    )
	A							  =      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(UpperCAmelCase    )
	A							  =      args.prompt if args.prompt else input("""Model prompt >>> """    )

	A							  =      tokenizer(UpperCAmelCase       ,			return_tensors="""pt"""       ,			add_special_tokens=UpperCAmelCase    )
	A							  =      {key: value.to(args.device    ) for key, value in inputs.items()}

	A							  =      model.generate(
	    **UpperCAmelCase       ,			max_length=args.length + len(inputs["""input_ids"""][0]    )       ,			penalty_alpha=args.penalty_alpha       ,			top_k=args.k       ,			)

	A							  =      []
	for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase    ):
		print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""    )
		A							  =      generated_sequence.tolist()

		# Decode text
		A							  =      tokenizer.decode(UpperCAmelCase       ,			clean_up_tokenization_spaces=UpperCAmelCase       ,			add_special_tokens=UpperCAmelCase    )

		# Remove all text after the stop token
		A							  =      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							  =      (
		    prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]       ,			clean_up_tokenization_spaces=UpperCAmelCase    )    ) :]
		)

		generated_sequences.append(UpperCAmelCase    )
		print(UpperCAmelCase    )

	return generated_sequences


if __name__ == "__main__":
				main()








'''simple docstring'''


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
		__lowerCAmelCase 				=    10







		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
			A							  =      {
			    """num_train_timesteps""": 1100,
			    """beta_start""": 0.0_001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			}

			config.update(**_lowerCAmelCase      )
			return config







		def        A     (self						:      Dict      ):
			for timesteps in [10, 50, 100, 1000]:
				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )







		def        A     (self						:      Optional[int]      ):
			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
				self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )







		def        A     (self						:      Tuple      ):
			for schedule in ["linear", "scaled_linear"]:
				self.check_over_configs(beta_schedule=_lowerCAmelCase      )







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
			assert abs(result_mean.item() - 18.0_331      ) < 5e-3







		def        A     (self						:      Union[str, Any]      ):
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=_lowerCAmelCase      )







		def        A     (self						:      Dict      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
			A							  =      sample.to(_lowerCAmelCase      )

			A							  =      torch.manual_seed(0      )

			for i, t in enumerate(scheduler.timesteps      ):
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 328.9_970      ) < 1e-2
			assert abs(result_mean.item() - 0.4_284      ) < 1e-3







		def        A     (self						:      Optional[int]      ):
			if torch_device == "mps":
				return
			A							  =      self.scheduler_classes[0]
			A							  =      self.get_scheduler_config()
			A							  =      scheduler_class(**_lowerCAmelCase      )

			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
			A							  =      torch.manual_seed(0      )

			A							  =      self.dummy_model()
			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )

				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
				A							  =      output.prev_sample

			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )

			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
			assert abs(result_mean.item() - 18.0_331      ) < 1e-3

