




# 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 				__snake_case     :
				lowerCAmelCase__			=   None
				lowerCAmelCase__			=   None
				lowerCAmelCase__			=   None



				@classmethod
				def 					SCREAMING_SNAKE_CASE				(					cls					:						Union[str, Any]					)     ->			Optional[int]:





								'''simple docstring'''




								return cls()






@dataclass
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   42
				lowerCAmelCase__			=   42
				lowerCAmelCase__			=   None






class 				__snake_case     (_a  ,		_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			int:





								'''simple docstring'''




								return True



				@register_to_config
				def __init__(					self					:						Union[str, Any]						,		_UpperCAmelCase					:						int = 2000						,		_UpperCAmelCase					:						float = 0.15						,		_UpperCAmelCase					:						float = 0.01						,		_UpperCAmelCase					:						float = 1348.0						,		_UpperCAmelCase					:						float = 1E-5						,		_UpperCAmelCase					:						int = 1						,		)     ->			Any:





								'''simple docstring'''




								pass



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					ScoreSdeVeSchedulerState.create()
								return self.set_sigmas(
								    UpperCAmelCase_						,		self.config.num_train_timesteps						,		self.config.sigma_min						,		self.config.sigma_max						,		self.config.sampling_eps						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						ScoreSdeVeSchedulerState						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						Tuple = ()						,		_UpperCAmelCase					:						float = None					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

								_lowerCAmelCase			:			List[str]        =					jnp.linspace(1						,		UpperCAmelCase_						,		UpperCAmelCase_					)
								return state.replace(timesteps=UpperCAmelCase_					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						ScoreSdeVeSchedulerState						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						float = None						,		_UpperCAmelCase					:						float = None						,		_UpperCAmelCase					:						float = None						,		)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					sigma_min if sigma_min is not None else self.config.sigma_min
								_lowerCAmelCase			:			Optional[Any]        =					sigma_max if sigma_max is not None else self.config.sigma_max
								_lowerCAmelCase			:			Tuple        =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if state.timesteps is None:
												_lowerCAmelCase			:			Any        =					self.set_timesteps(UpperCAmelCase_						,		UpperCAmelCase_						,		UpperCAmelCase_					)

								_lowerCAmelCase			:			Optional[Any]        =					jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase_					)						,		jnp.log(UpperCAmelCase_					)						,		UpperCAmelCase_					)					)
								_lowerCAmelCase			:			Dict        =					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

								return state.replace(discrete_sigmas=UpperCAmelCase_						,		sigmas=UpperCAmelCase_					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						List[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								return jnp.where(timesteps == 0						,		jnp.zeros_like(UpperCAmelCase_					)						,		state.discrete_sigmas[timesteps - 1]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]						,		_UpperCAmelCase					:						ScoreSdeVeSchedulerState						,		_UpperCAmelCase					:						jnp.ndarray						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						jnp.ndarray						,		_UpperCAmelCase					:						random.KeyArray						,		_UpperCAmelCase					:						bool = True						,		)     ->			str:





								'''simple docstring'''




								if state.timesteps is None:
												raise ValueError(
												    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""					)

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

								_lowerCAmelCase			:			Tuple        =					state.discrete_sigmas[timesteps]
								_lowerCAmelCase			:			Optional[int]        =					self.get_adjacent_sigma(UpperCAmelCase_						,		UpperCAmelCase_						,		UpperCAmelCase_					)
								_lowerCAmelCase			:			str        =					jnp.zeros_like(UpperCAmelCase_					)
								_lowerCAmelCase			:			Optional[int]        =					(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
								_lowerCAmelCase			:			Optional[int]        =					diffusion.flatten()
								_lowerCAmelCase			:			Optional[int]        =					broadcast_to_shape_from_left(UpperCAmelCase_						,		sample.shape					)
								_lowerCAmelCase			:			Union[str, Any]        =					drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								_lowerCAmelCase			:			List[Any]        =					random.split(UpperCAmelCase_						,		num=1					)
								_lowerCAmelCase			:			Union[str, Any]        =					random.normal(key=UpperCAmelCase_						,		shape=sample.shape					)
								_lowerCAmelCase			:			Any        =					sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								_lowerCAmelCase			:			Any        =					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=UpperCAmelCase_						,		prev_sample_mean=UpperCAmelCase_						,		state=UpperCAmelCase_					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						ScoreSdeVeSchedulerState						,		_UpperCAmelCase					:						jnp.ndarray						,		_UpperCAmelCase					:						jnp.ndarray						,		_UpperCAmelCase					:						random.KeyArray						,		_UpperCAmelCase					:						bool = True						,		)     ->			Tuple:





								'''simple docstring'''




								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
								_lowerCAmelCase			:			List[Any]        =					random.split(UpperCAmelCase_						,		num=1					)
								_lowerCAmelCase			:			Tuple        =					random.normal(key=UpperCAmelCase_						,		shape=sample.shape					)

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

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

								if not return_dict:
												return (prev_sample, state)

								return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_						,		state=UpperCAmelCase_					)



				def __len__(					self					:						Dict					)     ->			Tuple:





								'''simple docstring'''




								return self.config.num_train_timesteps









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      lowerCamelCase__	(   lowercase ):

  """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      lowerCamelCase__	(   ):

  """simple docstring"""






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

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

  parser.add_argument("--seed"  ,   type=lowercase  ,   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"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			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(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			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(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

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

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

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

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

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






