

'''simple docstring'''




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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	torch.FloatTensor
  __lowerCAmelCase   :	torch.FloatTensor




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Optional[int]=0.999    ,				SCREAMING_SNAKE_CASE :				str="cosine"    ,				):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE :				Any  ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(SCREAMING_SNAKE_CASE :				Any  ):
      return math.exp(t * -12.0  )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

  UpperCAmelCase  							=		[]
  for i in range(SCREAMING_SNAKE_CASE  ):
    UpperCAmelCase  							=		i / num_diffusion_timesteps
    UpperCAmelCase  							=		(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE  ) / alpha_bar_fn(SCREAMING_SNAKE_CASE  )    ,				SCREAMING_SNAKE_CASE  )  )
  return torch.tensor(SCREAMING_SNAKE_CASE    ,				dtype=torch.floataa  )



class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  __lowerCAmelCase   :	List[Any]      					=				1
  @register_to_config
  def __init__(       self				,	a_ = 1_0_0_0				,	a_ = 0.0001				,	a_ = 0.02				,	a_ = "linear"				,	a_ = 0.0				,	a_ = None				,	a_ = True				,	)							->			int:


    """simple docstring"""
    if trained_betas is not None:
      UpperCAmelCase  							=		torch.from_numpy(a_       )
    elif beta_schedule == "linear":
      UpperCAmelCase  							=		torch.linspace(a_				,	a_				,	a_				,	dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      UpperCAmelCase  							=		(
          torch.linspace(beta_start**0.5				,	beta_end**0.5				,	a_				,	dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      UpperCAmelCase  							=		betas_for_alpha_bar(a_       )
    elif beta_schedule == "sigmoid":
      # GeoDiff sigmoid schedule
      UpperCAmelCase  							=		torch.linspace(-6				,	6				,	a_       )
      UpperCAmelCase  							=		torch.sigmoid(a_       ) * (beta_end - beta_start) + beta_start
    else:
      raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

    UpperCAmelCase  							=		1.0 - self.betas
    UpperCAmelCase  							=		torch.cumprod(self.alphas				,	dim=0       )
    UpperCAmelCase  							=		torch.tensor(1.0       )

    UpperCAmelCase  							=		torch.tensor(1.0       )

    # standard deviation of the initial noise distribution
    UpperCAmelCase  							=		1.0

    # setable values
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		torch.from_numpy(np.arange(0				,	a_       )[::-1].copy()       )

    UpperCAmelCase  							=		eta
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			torch.FloatTensor:


    """simple docstring"""
    return sample
  def   snake_case_     (       self				,	a_				,	a_ = 1_0				,	a_ = 1_0				,	a_ = None				,	)							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		min(self.config.num_train_timesteps				,	a_       )
    UpperCAmelCase  							=		num_inference_steps

    UpperCAmelCase  							=		[]

    UpperCAmelCase  							=		{}
    for j in range(0				,	num_inference_steps - jump_length				,	a_       ):
      UpperCAmelCase  							=		jump_n_sample - 1

    UpperCAmelCase  							=		num_inference_steps
    while t >= 1:
      UpperCAmelCase  							=		t - 1
      timesteps.append(a_       )

      if jumps.get(a_				,	0       ) > 0:
        UpperCAmelCase  							=		jumps[t] - 1
        for _ in range(a_       ):
          UpperCAmelCase  							=		t + 1
          timesteps.append(a_       )

    UpperCAmelCase  							=		np.array(a_       ) * (self.config.num_train_timesteps // self.num_inference_steps)
    UpperCAmelCase  							=		torch.from_numpy(a_       ).to(a_       )
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		t - self.config.num_train_timesteps // self.num_inference_steps

    UpperCAmelCase  							=		self.alphas_cumprod[t]
    UpperCAmelCase  							=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    UpperCAmelCase  							=		1 - alpha_prod_t
    UpperCAmelCase  							=		1 - alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from
    # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
    # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
    # variance to pred_sample
    # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
    # without eta.
    # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
    UpperCAmelCase  							=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

    return variance
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	a_				,	a_ = None				,	a_ = True				,	)							->			Union[RePaintSchedulerOutput, Tuple]:


    """simple docstring"""
    UpperCAmelCase  							=		timestep
    UpperCAmelCase  							=		timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 1. compute alphas, betas
    UpperCAmelCase  							=		self.alphas_cumprod[t]
    UpperCAmelCase  							=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    UpperCAmelCase  							=		1 - alpha_prod_t

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    UpperCAmelCase  							=		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
      UpperCAmelCase  							=		torch.clamp(a_				,	-1				,	1       )

    # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
    # substitute formula (7) in the algorithm coming from DDPM paper
    # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
    # DDIM schedule gives the same results as DDPM with eta = 1.0
    # Noise is being reused in 7. and 8., but no impact on quality has
    # been observed.

    # 5. Add noise
    UpperCAmelCase  							=		model_output.device
    UpperCAmelCase  							=		randn_tensor(model_output.shape				,	generator=a_				,	device=a_				,	dtype=model_output.dtype       )
    UpperCAmelCase  							=		self.eta * self._get_variance(a_       ) ** 0.5

    UpperCAmelCase  							=		0
    if t > 0 and self.eta > 0:
      UpperCAmelCase  							=		std_dev_t * noise

    # 6. compute "direction pointing to x_t" of formula (12)
    # from https://arxiv.org/pdf/2010.02502.pdf
    UpperCAmelCase  							=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    UpperCAmelCase  							=		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

    # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
    UpperCAmelCase  							=		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

    # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
    UpperCAmelCase  							=		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

    if not return_dict:
      return (
          pred_prev_sample,
          pred_original_sample,
      )

    return RePaintSchedulerOutput(prev_sample=a_				,	pred_original_sample=a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_=None       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.config.num_train_timesteps // self.num_inference_steps

    for i in range(a_       ):
      UpperCAmelCase  							=		self.betas[timestep + i]
      if sample.device.type == "mps":
        # randn does not work reproducibly on mps
        UpperCAmelCase  							=		randn_tensor(sample.shape				,	dtype=sample.dtype				,	generator=a_       )
        UpperCAmelCase  							=		noise.to(sample.device       )
      else:
        UpperCAmelCase  							=		randn_tensor(sample.shape				,	generator=a_				,	device=sample.device				,	dtype=sample.dtype       )

      # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
      UpperCAmelCase  							=		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

    return sample
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	)							->			torch.FloatTensor:


    """simple docstring"""
    raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'       )





  def __len__(       self       )							->			Optional[int]:


    """simple docstring"""
    return self.config.num_train_timesteps





'''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,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  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()


