


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,
)
_snake_case   = logging.getLogger(__name__)







def   A   (		A__ ):
					np.random.seed(args.seed )
					torch.manual_seed(args.seed )
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed )







def   A   (		):
					UpperCAmelCase						:				str		  =			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=2_0 )
					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=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						:				int		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
					UpperCAmelCase						:				List[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(A__ )

					# Initialize the model and tokenizer
					UpperCAmelCase						:				Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
					UpperCAmelCase						:				Union[str, Any]		  =			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__ )
					UpperCAmelCase						:				Optional[Any]		  =			args.prompt if args.prompt else input("Model prompt >>> " )

					UpperCAmelCase						:				Optional[int]		  =			tokenizer(A__		,		return_tensors="pt"		,		add_special_tokens=A__ )
					UpperCAmelCase						:				Any		  =			{key: value.to(args.device ) for key, value in inputs.items()}

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

					UpperCAmelCase						:				Any		  =			[]
					for generated_sequence_idx, generated_sequence in enumerate(A__ ):
										print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
										UpperCAmelCase						:				int		  =			generated_sequence.tolist()

										# Decode text
										UpperCAmelCase						:				Optional[Any]		  =			tokenizer.decode(A__		,		clean_up_tokenization_spaces=A__		,		add_special_tokens=A__ )

										# 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						:				Tuple		  =			(
										    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()







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      UpperCAmelCase						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

      UpperCAmelCase						:				List[str]		  =			state.discrete_sigmas[timesteps]
      UpperCAmelCase						:				Optional[int]		  =			self.get_adjacent_sigma(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			jnp.zeros_like(_a				)
      UpperCAmelCase						:				List[Any]		  =			(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase						:				Union[str, Any]		  =			diffusion.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				Tuple		  =			drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase						:				str		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				Optional[int]		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				int		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase						:				List[Any]		  =			step_size * jnp.ones(sample.shape[0]				)

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps



