
'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=0.999   ,	UpperCAmelCase="cosine"   ,	)->      str:







      """simple docstring"""
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

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


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Optional[Any]    					=       [e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase__     :     Tuple    					=       2






 @register_to_config
 def __init__(      self	:Any						,					_A	:int = 1_000						,					_A	:float = 0.00_085						,					_A	:float = 0.012						,					_A	:str = "linear"						,					_A	:Optional[Union[np.ndarray, List[float]]] = None						,					_A	:str = "epsilon"						,					_A	:str = "linspace"						,					_A	:int = 0						,					)					->      List[Any]:







       '''simple docstring'''



       if trained_betas is not None:
             __A													=      torch.tensor(_A						,					dtype=torch.floataa      )
       elif beta_schedule == "linear":
             __A													=      torch.linspace(_A						,					_A						,					_A						,					dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __A													=      (
                 torch.linspace(beta_start**0.5						,					beta_end**0.5						,					_A						,					dtype=torch.floataa      ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __A													=      betas_for_alpha_bar(_A      )
       else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'      )

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

       #  set all values
       self.set_timesteps(_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:Any						,					_A	:List[str]						,					_A	:Optional[Any]=None      )					->      Tuple:







       '''simple docstring'''



       if schedule_timesteps is None:
             __A													=      self.timesteps

       __A													=      (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter      ) == 0:
             __A													=      1 if len(_A      ) > 1 else 0
       else:
             __A													=      timestep.cpu().item() if torch.is_tensor(_A      ) else timestep
             __A													=      self._index_counter[timestep_int]

       return indices[pos].item()






 @property
 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5






 def 						lowercase_    (      self	:Tuple						,					_A	:torch.FloatTensor						,					_A	:Union[float, torch.FloatTensor]						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

       if self.state_in_first_order:
             __A													=      self.sigmas[step_index]
       else:
             __A													=      self.sigmas_interpol[step_index - 1]

       __A													=      sample / ((sigma**2 + 1) ** 0.5)
       return sample






 def 						lowercase_    (      self	:str						,					_A	:int						,					_A	:Union[str, torch.device] = None						,					_A	:Optional[int] = None						,					)					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      num_inference_steps

       __A													=      num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             __A													=      np.linspace(0						,					num_train_timesteps - 1						,					_A						,					dtype=_A      )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             __A													=      num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A													=      (np.arange(0						,					_A      ) * step_ratio).round()[::-1].copy().astype(_A      )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             __A													=      num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A													=      (np.arange(_A						,					0						,					-step_ratio      )).round().copy().astype(_A      )
             timesteps -= 1
       else:
             raise ValueError(
                 F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

       __A													=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
       __A													=      torch.from_numpy(np.log(_A      )      ).to(_A      )

       __A													=      np.interp(_A						,					np.arange(0						,					len(_A      )      )						,					_A      )
       __A													=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
       __A													=      torch.from_numpy(_A      ).to(device=_A      )

       # compute up and down sigmas
       __A													=      sigmas.roll(-1      )
       __A													=      0.0
       __A													=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       __A													=      (sigmas_next**2 - sigmas_up**2) ** 0.5
       __A													=      0.0

       # compute interpolated sigmas
       __A													=      sigmas.log().lerp(sigmas_down.log()						,					0.5      ).exp()
       __A													=      0.0

       # set sigmas
       __A													=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
       __A													=      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
       __A													=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
       __A													=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

       if str(_A      ).startswith('mps'      ):
             # mps does not support float64
             __A													=      torch.from_numpy(_A      ).to(_A						,					dtype=torch.floataa      )
       else:
             __A													=      torch.from_numpy(_A      ).to(_A      )

       __A													=      self.sigma_to_t(_A      ).to(_A						,					dtype=timesteps.dtype      )
       __A													=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,					dim=-1      ).flatten()

       __A													=      torch.cat([timesteps[:1], interleaved_timesteps]      )

       __A													=      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       __A													=      defaultdict(_A      )






 def 						lowercase_    (      self	:str						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      sigma.log()

       # get distribution
       __A													=      log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       __A													=      dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
       __A													=      low_idx + 1

       __A													=      self.log_sigmas[low_idx]
       __A													=      self.log_sigmas[high_idx]

       # interpolate sigmas
       __A													=      (low - log_sigma) / (low - high)
       __A													=      w.clamp(0						,					1      )

       # transform interpolation to time range
       __A													=      (1 - w) * low_idx + w * high_idx
       __A													=      t.view(sigma.shape      )
       return t






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      Optional[int]:







       '''simple docstring'''



       return self.sample is None






 def 						lowercase_    (      self	:Any						,					_A	:Union[torch.FloatTensor, np.ndarray]						,					_A	:Union[float, torch.FloatTensor]						,					_A	:Union[torch.FloatTensor, np.ndarray]						,					_A	:Optional[torch.Generator] = None						,					_A	:bool = True						,					)					->      Union[SchedulerOutput, Tuple]:







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

       # advance index counter by 1
       __A													=      timestep.cpu().item() if torch.is_tensor(_A      ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             __A													=      self.sigmas[step_index]
             __A													=      self.sigmas_interpol[step_index]
             __A													=      self.sigmas_up[step_index]
             __A													=      self.sigmas_down[step_index - 1]
       else:
             # 2nd order / KPDM2's method
             __A													=      self.sigmas[step_index - 1]
             __A													=      self.sigmas_interpol[step_index - 1]
             __A													=      self.sigmas_up[step_index - 1]
             __A													=      self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       __A													=      0
       __A													=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       __A													=      model_output.device
       __A													=      randn_tensor(model_output.shape						,					dtype=model_output.dtype						,					device=_A						,					generator=_A      )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
       elif self.config.prediction_type == "sample":
             raise NotImplementedError('prediction_type not implemented yet: sample'      )
       else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             __A													=      (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             __A													=      sigma_interpol - sigma_hat

             # store for 2nd order step
             __A													=      sample
             __A													=      dt
             __A													=      sample + derivative * dt
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             __A													=      (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             __A													=      sigma_down - sigma_hat

             __A													=      self.sample
             __A													=      None

             __A													=      sample + derivative * dt
             __A													=      prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_A      )






 def 						lowercase_    (      self	:List[str]						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.sigmas.to(device=original_samples.device						,					dtype=original_samples.dtype      )
       if original_samples.device.type == "mps" and torch.is_floating_point(_A      ):
             # mps does not support float64
             __A													=      self.timesteps.to(original_samples.device						,					dtype=torch.floataa      )
             __A													=      timesteps.to(original_samples.device						,					dtype=torch.floataa      )
       else:
             __A													=      self.timesteps.to(original_samples.device      )
             __A													=      timesteps.to(original_samples.device      )

       __A													=      [self.index_for_timestep(_A						,					_A      ) for t in timesteps]

       __A													=      sigmas[step_indices].flatten()
       while len(sigma.shape      ) < len(original_samples.shape      ):
             __A													=      sigma.unsqueeze(-1      )

       __A													=      original_samples + noise * sigma
       return noisy_samples






 def __len__(      self	:Union[str, Any]      )					->      Tuple:







       '''simple docstring'''



       return self.config.num_train_timesteps














'''simple docstring'''





import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
__A				:				List[str]					=		logging.getLogger(__name__)

__A				:				Dict					=		int(1_00_00)  # Hardcoded max length to avoid infinite loop

__A				:				Dict					=		{
    """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
    """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
    """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
    """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
    """gptj""": (GPTJForCausalLM, AutoTokenizer),
    """bloom""": (BloomForCausalLM, BloomTokenizerFast),
    """llama""": (LlamaForCausalLM, LlamaTokenizer),
    """opt""": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
__A				:				Union[str, Any]					=		"""In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""






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






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__					,  lowercase__):
 if args.temperature > 0.7:
  logger.info("CTRL typically works better with lower temperatures (and lower top_k).")

 lowerCamelCase__	=  tokenizer.encode(lowercase__					,  add_special_tokens=lowercase__)
 if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
  logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
 return prompt_text






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__					,  lowercase__):
 # kwargs = {"language": None, "mask_token_id": None}

 # Set the language
 lowerCamelCase__	=  hasattr(model.config					,  "use_lang_emb") and model.config.use_lang_emb
 if hasattr(model.config					,  "lang2id") and use_lang_emb:
  lowerCamelCase__	=  model.config.langaid.keys()
  if args.xlm_language in available_languages:
   lowerCamelCase__	=  args.xlm_language
  else:
   lowerCamelCase__	=  None
   while language not in available_languages:
    lowerCamelCase__	=  input("Using XLM. Select language in " + str(list(lowercase__)) + " >>> ")

  lowerCamelCase__	=  model.config.langaid[language]
  # kwargs["language"] = tokenizer.lang2id[language]

 # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
 # XLM masked-language modeling (MLM) models need masked token
 # is_xlm_mlm = "mlm" in args.model_name_or_path
 # if is_xlm_mlm:
 #     kwargs["mask_token_id"] = tokenizer.mask_token_id

 return prompt_text






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__					,  lowercase__):
 lowerCamelCase__	=  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 lowerCamelCase__	=  prefix + prompt_text
 return prompt_text






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__					,  lowercase__):
 lowerCamelCase__	=  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 lowerCamelCase__	=  prefix + prompt_text
 return prompt_text


__A				:				Optional[Any]					=		{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}






def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 if length < 0 and max_sequence_length > 0:
  lowerCamelCase__	=  max_sequence_length
 elif 0 < max_sequence_length < length:
  lowerCamelCase__	=  max_sequence_length  # No generation bigger than model size
 elif length < 0:
  lowerCamelCase__	=  MAX_LENGTH  # avoid infinite loop
 return length






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  None
 if hasattr(lowercase__					,  "hidden_size"):
  lowerCamelCase__	=  model_config.hidden_size
 elif hasattr(lowercase__					,  "n_embed"):
  lowerCamelCase__	=  model_config.n_embed
 elif hasattr(lowercase__					,  "n_embd"):
  lowerCamelCase__	=  model_config.n_embd

 lowerCamelCase__	=  None
 if hasattr(lowercase__					,  "num_attention_heads"):
  lowerCamelCase__	=  model_config.num_attention_heads
 elif hasattr(lowercase__					,  "n_head"):
  lowerCamelCase__	=  model_config.n_head

 if embedding_size is None or num_head is None or num_head == 0:
  raise ValueError("Check the model config")

 lowerCamelCase__	=  int(embedding_size / num_head)
 if hasattr(lowercase__					,  "n_layer"):
  lowerCamelCase__	=  model_config.n_layer
 elif hasattr(lowercase__					,  "num_hidden_layers"):
  lowerCamelCase__	=  model_config.num_hidden_layers
 else:
  raise ValueError("Number of hidden layers couldn't be determined from the model config")

 return num_layer, num_head, num_embedding_size_per_head






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	,						lowerCamelCase__	=  sparse_model_config(model.config)
 if model.config.model_type == "bloom":
  lowerCamelCase__	=  tuple(
      (
          torch.empty(int(num_attention_heads * batch_size)					,  lowercase__					,  lowercase__)
          .to(model.dtype)
          .to(model.device),
          torch.empty(int(num_attention_heads * batch_size)					,  lowercase__					,  lowercase__)
          .to(model.dtype)
          .to(model.device),
      )
      for _ in range(lowercase__))
 else:
  lowerCamelCase__	=  tuple(
      (
          torch.empty(lowercase__					,  lowercase__					,  lowercase__					,  lowercase__)
          .to(model.dtype)
          .to(model.device),
          torch.empty(lowercase__					,  lowercase__					,  lowercase__					,  lowercase__)
          .to(model.dtype)
          .to(model.device),
      )
      for _ in range(lowercase__))
 return past_key_values






def  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__):
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  tokenizer.batch_encode_plus(lowercase__					,  return_tensors="pt")
 lowerCamelCase__	=  dummy_input.to(model.device)
 if model.config.use_cache:
  lowerCamelCase__	=  generate_past_key_values(lowercase__					,  lowercase__					,  1)
 lowerCamelCase__	=  torch.cat(
     [
         torch.zeros(dummy_input["attention_mask"].shape[0]					,  1)
         .to(dummy_input["attention_mask"].dtype)
         .to(model.device),
         dummy_input["attention_mask"],
     ]					,  -1					,  )
 return dummy_input
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				("_optimized", "_default")

    def __init__(       self  :  Union[str, Any]					,  __lowerCamelCase  :  Optional[Any]					,  __lowerCamelCase  :  Union[str, Any]  )				->				Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__	=  optimized
     lowerCamelCase__	=  default

    def __call__(       self  :  List[str]					,  *__lowerCamelCase  :  str					,  **__lowerCamelCase  :  Dict  )				->				Union[str, Any]:

     '''simple docstring'''




     if kwargs["past_key_values"] is None and self._default.config.use_cache:
      lowerCamelCase__	=  generate_past_key_values(self._default					,  kwargs["input_ids"].shape[0]					,  0  )
     kwargs.pop("position_ids"					,  __lowerCamelCase  )
     for k in list(kwargs.keys()  ):
      if kwargs[k] is None or isinstance(kwargs[k]					,  __lowerCamelCase  ):
       kwargs.pop(__lowerCamelCase  )
     lowerCamelCase__	=  self._optimized(**__lowerCamelCase  )
     lowerCamelCase__	=  outputs[0]
     lowerCamelCase__	=  outputs[1]
     lowerCamelCase__	=  CausalLMOutputWithPast(
         loss=__lowerCamelCase					,  logits=__lowerCamelCase					,  past_key_values=__lowerCamelCase					,  hidden_states=__lowerCamelCase					,  attentions=__lowerCamelCase					,  )
     return fixed_output

    def __getattr__(       self  :  str					,  __lowerCamelCase  :  Dict  )				->				Any:

     '''simple docstring'''




     return getattr(self._default					,  __lowerCamelCase  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  Optional[int]=None					,  __lowerCamelCase  :  List[str]=None					,  __lowerCamelCase  :  Tuple=None					,  **__lowerCamelCase  :  List[str]  )				->				int:

     '''simple docstring'''




     return self._default.prepare_inputs_for_generation(
         __lowerCamelCase					,  past_key_values=__lowerCamelCase					,  inputs_embeds=__lowerCamelCase					,  use_cache=__lowerCamelCase					,  **__lowerCamelCase  )




    def  a__					(       self  :  Any					,  __lowerCamelCase  :  Tuple[Tuple[torch.Tensor]]					,  __lowerCamelCase  :  torch.Tensor  )				->				Tuple[Tuple[torch.Tensor]]:

     '''simple docstring'''




     return self._default._reorder_cache(__lowerCamelCase					,  __lowerCamelCase  )






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  argparse.ArgumentParser()
 parser.add_argument(
     "--model_type"					,  default=lowercase__					,  type=lowercase__					,  required=lowercase__					,  help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())					,  )
 parser.add_argument(
     "--model_name_or_path"					,  default=lowercase__					,  type=lowercase__					,  required=lowercase__					,  help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys())					,  )

 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("--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("--num_return_sequences"					,  type=lowercase__					,  default=1					,  help="The number of samples to generate.")
 parser.add_argument(
     "--fp16"					,  action="store_true"					,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"					,  )
 parser.add_argument("--jit"					,  action="store_true"					,  help="Whether or not to use jit trace to accelerate inference")
 lowerCamelCase__	=  parser.parse_args()

 lowerCamelCase__	=  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
 lowerCamelCase__	=  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
 try:
  lowerCamelCase__	=  args.model_type.lower()
  lowerCamelCase__	,						lowerCamelCase__	=  MODEL_CLASSES[args.model_type]
 except KeyError:
  raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)")

 lowerCamelCase__	=  tokenizer_class.from_pretrained(args.model_name_or_path)
 if tokenizer.pad_token is None:
  lowerCamelCase__	=  tokenizer.eos_token
 lowerCamelCase__	=  model_class.from_pretrained(args.model_name_or_path)
 model.to(args.device)

 if args.fpaa:
  model.half()
 lowerCamelCase__	=  getattr(model.config					,  "max_position_embeddings"					,  0)
 lowerCamelCase__	=  adjust_length_to_model(args.length					,  max_sequence_length=lowercase__)
 logger.info(lowercase__)

 lowerCamelCase__	=  args.prompt if args.prompt else input("Model prompt >>> ")

 # Different models need different input formatting and/or extra arguments
 lowerCamelCase__	=  args.model_type in PREPROCESSING_FUNCTIONS.keys()
 if requires_preprocessing:
  lowerCamelCase__	=  PREPROCESSING_FUNCTIONS.get(args.model_type)
  lowerCamelCase__	=  prepare_input(lowercase__					,  lowercase__					,  lowercase__					,  lowercase__)

  if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
   lowerCamelCase__	=  {"add_space_before_punct_symbol": True}
  else:
   lowerCamelCase__	=  {}

  lowerCamelCase__	=  tokenizer.encode(
      lowercase__					,  add_special_tokens=lowercase__					,  return_tensors="pt"					,  **lowercase__)
 else:
  lowerCamelCase__	=  args.prefix if args.prefix else args.padding_text
  lowerCamelCase__	=  tokenizer.encode(prefix + prompt_text					,  add_special_tokens=lowercase__					,  return_tensors="pt")
 lowerCamelCase__	=  encoded_prompt.to(args.device)

 if encoded_prompt.size()[-1] == 0:
  lowerCamelCase__	=  None
 else:
  lowerCamelCase__	=  encoded_prompt

 if args.jit:
  lowerCamelCase__	=  ["enable jit"]
  lowerCamelCase__	=  prepare_jit_inputs(lowercase__					,  lowercase__					,  lowercase__)
  torch._C._jit_set_texpr_fuser_enabled(lowercase__)
  lowerCamelCase__	=  False
  if hasattr(lowercase__					,  "forward"):
   lowerCamelCase__	=  inspect.signature(model.forward)
  else:
   lowerCamelCase__	=  inspect.signature(model.__call__)
  lowerCamelCase__	=  tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowercase__					,  lowercase__) is not None)
  lowerCamelCase__	=  torch.jit.trace(lowercase__					,  lowercase__					,  strict=lowercase__)
  lowerCamelCase__	=  torch.jit.freeze(traced_model.eval())
  traced_model(*lowercase__)
  traced_model(*lowercase__)

  lowerCamelCase__	=  _ModelFallbackWrapper(lowercase__					,  lowercase__)

 lowerCamelCase__	=  model.generate(
     input_ids=lowercase__					,  max_length=args.length + len(encoded_prompt[0])					,  temperature=args.temperature					,  top_k=args.k					,  top_p=args.p					,  repetition_penalty=args.repetition_penalty					,  do_sample=lowercase__					,  num_return_sequences=args.num_return_sequences					,  )

 # Remove the batch dimension when returning multiple sequences
 if len(output_sequences.shape) > 2:
  output_sequences.squeeze_()

 lowerCamelCase__	=  []

 for generated_sequence_idx, generated_sequence in enumerate(lowercase__):
  print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''')
  lowerCamelCase__	=  generated_sequence.tolist()

  # Decode text
  lowerCamelCase__	=  tokenizer.decode(lowercase__					,  clean_up_tokenization_spaces=lowercase__)

  # Remove all text after the stop token
  lowerCamelCase__	=  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
  lowerCamelCase__	=  (
      prompt_text + text[len(tokenizer.decode(encoded_prompt[0]					,  clean_up_tokenization_spaces=lowercase__)) :]
  )

  generated_sequences.append(lowercase__)
  print(lowercase__)

 return generated_sequences


if __name__ == "__main__":
 main()

