

import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class A__     (			nn.Module			):



 """simple docstring"""







 def __init__(		self			:						str  ,    A_			:						Union[str, Any]  ,    A_			:						Tuple=1.0							):






      '''simple docstring'''




      super().__init__()

      _lowerCAmelCase :       str	   =     cut_size
      _lowerCAmelCase :       Tuple	   =     cut_power


 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						Tuple  ,    A_			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[Any]	   =     pixel_values.shape[2:4]
      _lowerCAmelCase :       Optional[Any]	   =     min(A_  ,    A_							)
      _lowerCAmelCase :       Union[str, Any]	   =     min(A_  ,    A_  ,    self.cut_size							)
      _lowerCAmelCase :       Tuple	   =     []
      for _ in range(A_							):
           _lowerCAmelCase :       List[Any]	   =     int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
           _lowerCAmelCase :       Union[str, Any]	   =     torch.randint(0  ,    sideX - size + 1  ,    ()							)
           _lowerCAmelCase :       List[str]	   =     torch.randint(0  ,    sideY - size + 1  ,    ()							)
           _lowerCAmelCase :       Optional[int]	   =     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
           cutouts.append(F.adaptive_avg_poolad(A_  ,    self.cut_size							)							)
      return torch.cat(A_							)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   str:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     F.normalize(SCREAMING_SNAKE_CASE		,				dim=-1					)
     _lowerCAmelCase :       Optional[Any]	   =     F.normalize(SCREAMING_SNAKE_CASE		,				dim=-1					)
     return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     for param in model.parameters():
          _lowerCAmelCase :       Any	   =     value





class A__     (			A			):



 """simple docstring"""







 def __init__(		self			:						Tuple  ,    A_			:						AutoencoderKL  ,    A_			:						CLIPTextModel  ,    A_			:						CLIPModel  ,    A_			:						CLIPTokenizer  ,    A_			:						UNetaDConditionModel  ,    A_			:						Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]  ,    A_			:						CLIPImageProcessor  ,    ):






      '''simple docstring'''




      super().__init__()
      self.register_modules(
          vae=A_  ,    text_encoder=A_  ,    clip_model=A_  ,    tokenizer=A_  ,    unet=A_  ,    scheduler=A_  ,    feature_extractor=A_  ,    )

      _lowerCAmelCase :       List[Any]	   =     transforms.Normalize(mean=feature_extractor.image_mean  ,    std=feature_extractor.image_std							)
      _lowerCAmelCase :       Any	   =     (
          feature_extractor.size
          if isinstance(feature_extractor.size  ,    A_							)
          else feature_extractor.size["shortest_edge"]
      )
      _lowerCAmelCase :       int	   =     MakeCutouts(self.cut_out_size							)

      set_requires_grad(self.text_encoder  ,    A_							)
      set_requires_grad(self.clip_model  ,    A_							)

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						Optional[Union[str, int]] = "auto"							):






      '''simple docstring'''




      if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _lowerCAmelCase :       Dict	   =     self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(A_							)

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      self.enable_attention_slicing(A_							)

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      set_requires_grad(self.vae  ,    A_							)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      set_requires_grad(self.vae  ,    A_							)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      set_requires_grad(self.unet  ,    A_							)

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      set_requires_grad(self.unet  ,    A_							)

 @torch.enable_grad()
 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						List[str]  ,    A_			:						List[str]  ,    A_			:						str  ,    A_			:						List[Any]  ,    A_			:						int  ,    A_			:						str  ,    A_			:						Any  ,    A_			:						Dict  ,    A_			:						Dict=True  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     latents.detach().requires_grad_()

      _lowerCAmelCase :       List[str]	   =     self.scheduler.scale_model_input(A_  ,    A_							)

      # predict the noise residual
      _lowerCAmelCase :       Union[str, Any]	   =     self.unet(A_  ,    A_  ,    encoder_hidden_states=A_							).sample

      if isinstance(self.scheduler  ,    (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
           _lowerCAmelCase :       Any	   =     self.scheduler.alphas_cumprod[timestep]
           _lowerCAmelCase :       Any	   =     1 - alpha_prod_t
           # compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _lowerCAmelCase :       Any	   =     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

           _lowerCAmelCase :       List[Any]	   =     torch.sqrt(A_							)
           _lowerCAmelCase :       Any	   =     pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler  ,    A_							):
           _lowerCAmelCase :       Optional[int]	   =     self.scheduler.sigmas[index]
           _lowerCAmelCase :       int	   =     latents - sigma * noise_pred
      else:
           raise ValueError(F'''scheduler type {type(self.scheduler							)} not supported'''							)

      _lowerCAmelCase :       Tuple	   =     1 / self.vae.config.scaling_factor * sample
      _lowerCAmelCase :       int	   =     self.vae.decode(A_							).sample
      _lowerCAmelCase :       List[str]	   =     (image / 2 + 0.5).clamp(0  ,    1							)

      if use_cutouts:
           _lowerCAmelCase :       str	   =     self.make_cutouts(A_  ,    A_							)
      else:
           _lowerCAmelCase :       List[str]	   =     transforms.Resize(self.cut_out_size							)(A_							)
      _lowerCAmelCase :       str	   =     self.normalize(A_							).to(latents.dtype							)

      _lowerCAmelCase :       Optional[int]	   =     self.clip_model.get_image_features(A_							)
      _lowerCAmelCase :       int	   =     image_embeddings_clip / image_embeddings_clip.norm(p=2  ,    dim=-1  ,    keepdim=A_							)

      if use_cutouts:
           _lowerCAmelCase :       Any	   =     spherical_dist_loss(A_  ,    A_							)
           _lowerCAmelCase :       Dict	   =     dists.view([num_cutouts, sample.shape[0], -1]							)
           _lowerCAmelCase :       List[Any]	   =     dists.sum(2							).mean(0							).sum() * clip_guidance_scale
      else:
           _lowerCAmelCase :       List[Any]	   =     spherical_dist_loss(A_  ,    A_							).mean() * clip_guidance_scale

      _lowerCAmelCase :       List[Any]	   =     -torch.autograd.grad(A_  ,    A_							)[0]

      if isinstance(self.scheduler  ,    A_							):
           _lowerCAmelCase :       str	   =     latents.detach() + grads * (sigma**2)
           _lowerCAmelCase :       Union[str, Any]	   =     noise_pred_original
      else:
           _lowerCAmelCase :       Dict	   =     noise_pred_original - torch.sqrt(A_							) * grads
      return noise_pred, latents


 @torch.no_grad()
 def __call__(		self			:						List[Any]  ,    A_			:						Union[str, List[str]]  ,    A_			:						Optional[int] = 5_1_2  ,    A_			:						Optional[int] = 5_1_2  ,    A_			:						Optional[int] = 5_0  ,    A_			:						Optional[float] = 7.5  ,    A_			:						Optional[int] = 1  ,    A_			:						float = 0.0  ,    A_			:						Optional[float] = 1_0_0  ,    A_			:						Optional[Union[str, List[str]]] = None  ,    A_			:						Optional[int] = 4  ,    A_			:						Optional[bool] = True  ,    A_			:						Optional[torch.Generator] = None  ,    A_			:						Optional[torch.FloatTensor] = None  ,    A_			:						Optional[str] = "pil"  ,    A_			:						bool = True  ,    ):






      '''simple docstring'''




      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       List[Any]	   =     1
      elif isinstance(A_  ,    A_							):
           _lowerCAmelCase :       Tuple	   =     len(A_							)
      else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_							)}'''							)

      if height % 8 != 0 or width % 8 != 0:
           raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

      # get prompt text embeddings
      _lowerCAmelCase :       Union[str, Any]	   =     self.tokenizer(
          A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    )
      _lowerCAmelCase :       Dict	   =     self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
      # duplicate text embeddings for each generation per prompt
      _lowerCAmelCase :       Tuple	   =     text_embeddings.repeat_interleave(A_  ,    dim=0							)

      if clip_guidance_scale > 0:
           if clip_prompt is not None:
                _lowerCAmelCase :       Optional[int]	   =     self.tokenizer(
                    A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    ).input_ids.to(self.device							)
           else:
                _lowerCAmelCase :       Tuple	   =     text_input.input_ids.to(self.device							)
           _lowerCAmelCase :       int	   =     self.clip_model.get_text_features(A_							)
           _lowerCAmelCase :       Any	   =     text_embeddings_clip / text_embeddings_clip.norm(p=2  ,    dim=-1  ,    keepdim=A_							)
           # duplicate text embeddings clip for each generation per prompt
           _lowerCAmelCase :       int	   =     text_embeddings_clip.repeat_interleave(A_  ,    dim=0							)

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      _lowerCAmelCase :       Union[str, Any]	   =     guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _lowerCAmelCase :       List[str]	   =     text_input.input_ids.shape[-1]
           _lowerCAmelCase :       int	   =     self.tokenizer([""]  ,    padding="max_length"  ,    max_length=A_  ,    return_tensors="pt"							)
           _lowerCAmelCase :       Dict	   =     self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
           # duplicate unconditional embeddings for each generation per prompt
           _lowerCAmelCase :       Optional[int]	   =     uncond_embeddings.repeat_interleave(A_  ,    dim=0							)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           _lowerCAmelCase :       Tuple	   =     torch.cat([uncond_embeddings, text_embeddings]							)

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      _lowerCAmelCase :       Optional[Any]	   =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      _lowerCAmelCase :       Tuple	   =     text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _lowerCAmelCase :       str	   =     torch.randn(A_  ,    generator=A_  ,    device="cpu"  ,    dtype=A_							).to(
                    self.device							)
           else:
                _lowerCAmelCase :       List[Any]	   =     torch.randn(A_  ,    generator=A_  ,    device=self.device  ,    dtype=A_							)
      else:
           if latents.shape != latents_shape:
                raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
           _lowerCAmelCase :       Optional[Any]	   =     latents.to(self.device							)

      # set timesteps
      _lowerCAmelCase :       int	   =     "offset" in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
      _lowerCAmelCase :       int	   =     {}
      if accepts_offset:
           _lowerCAmelCase :       Dict	   =     1

      self.scheduler.set_timesteps(A_  ,    **A_							)

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      _lowerCAmelCase :       Union[str, Any]	   =     self.scheduler.timesteps.to(self.device							)

      # scale the initial noise by the standard deviation required by the scheduler
      _lowerCAmelCase :       List[str]	   =     latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _lowerCAmelCase :       int	   =     "eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
      _lowerCAmelCase :       Optional[int]	   =     {}
      if accepts_eta:
           _lowerCAmelCase :       Dict	   =     eta

      # check if the scheduler accepts generator
      _lowerCAmelCase :       Optional[Any]	   =     "generator" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
      if accepts_generator:
           _lowerCAmelCase :       int	   =     generator

      for i, t in enumerate(self.progress_bar(A_							)							):
           # expand the latents if we are doing classifier free guidance
           _lowerCAmelCase :       Dict	   =     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
           _lowerCAmelCase :       Optional[Any]	   =     self.scheduler.scale_model_input(A_  ,    A_							)

           # predict the noise residual
           _lowerCAmelCase :       Optional[Any]	   =     self.unet(A_  ,    A_  ,    encoder_hidden_states=A_							).sample

           # perform classifier free guidance
           if do_classifier_free_guidance:
                _lowerCAmelCase   ,      _lowerCAmelCase :       List[Any]	   =     noise_pred.chunk(2							)
                _lowerCAmelCase :       Optional[int]	   =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # perform clip guidance
           if clip_guidance_scale > 0:
                _lowerCAmelCase :       Any	   =     (
                    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
                )
                _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     self.cond_fn(
                    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    )

           # compute the previous noisy sample x_t -> x_t-1
           _lowerCAmelCase :       Tuple	   =     self.scheduler.step(A_  ,    A_  ,    A_  ,    **A_							).prev_sample

      # scale and decode the image latents with vae
      _lowerCAmelCase :       Optional[Any]	   =     1 / self.vae.config.scaling_factor * latents
      _lowerCAmelCase :       Any	   =     self.vae.decode(A_							).sample

      _lowerCAmelCase :       List[str]	   =     (image / 2 + 0.5).clamp(0  ,    1							)
      _lowerCAmelCase :       Optional[int]	   =     image.cpu().permute(0  ,    2  ,    3  ,    1							).numpy()

      if output_type == "pil":
           _lowerCAmelCase :       Optional[Any]	   =     self.numpy_to_pil(A_							)

      if not return_dict:
           return (image, None)

      return StableDiffusionPipelineOutput(images=A_  ,    nsfw_content_detected=A_							)









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,
)
__UpperCAmelCase     					=  logging.getLogger(__name__)

__UpperCAmelCase     					=  int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

__UpperCAmelCase     					=  {
    '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
__UpperCAmelCase     					=  'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   str:

     """simple docstring"""







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

     _lowerCAmelCase :       Optional[int]	   =     tokenizer.encode(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE					)
     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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     hasattr(model.config		,				"use_lang_emb"					) and model.config.use_lang_emb
     if hasattr(model.config		,				"lang2id"					) and use_lang_emb:
          _lowerCAmelCase :       List[Any]	   =     model.config.langaid.keys()
          if args.xlm_language in available_languages:
               _lowerCAmelCase :       Optional[int]	   =     args.xlm_language
          else:
               _lowerCAmelCase :       Dict	   =     None
               while language not in available_languages:
                    _lowerCAmelCase :       Dict	   =     input("Using XLM. Select language in " + str(list(SCREAMING_SNAKE_CASE					)					) + " >>> "					)

          _lowerCAmelCase :       Any	   =     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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     _lowerCAmelCase :       Union[str, Any]	   =     prefix + prompt_text
     return prompt_text







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     _lowerCAmelCase :       Any	   =     prefix + prompt_text
     return prompt_text


__UpperCAmelCase     					=  {
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     if length < 0 and max_sequence_length > 0:
          _lowerCAmelCase :       int	   =     max_sequence_length
     elif 0 < max_sequence_length < length:
          _lowerCAmelCase :       Optional[int]	   =     max_sequence_length  # No generation bigger than model size
     elif length < 0:
          _lowerCAmelCase :       Optional[Any]	   =     MAX_LENGTH  # avoid infinite loop
     return length







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       int	   =     None
     if hasattr(SCREAMING_SNAKE_CASE		,				"hidden_size"					):
          _lowerCAmelCase :       str	   =     model_config.hidden_size
     elif hasattr(SCREAMING_SNAKE_CASE		,				"n_embed"					):
          _lowerCAmelCase :       Optional[int]	   =     model_config.n_embed
     elif hasattr(SCREAMING_SNAKE_CASE		,				"n_embd"					):
          _lowerCAmelCase :       Dict	   =     model_config.n_embd

     _lowerCAmelCase :       Union[str, Any]	   =     None
     if hasattr(SCREAMING_SNAKE_CASE		,				"num_attention_heads"					):
          _lowerCAmelCase :       List[Any]	   =     model_config.num_attention_heads
     elif hasattr(SCREAMING_SNAKE_CASE		,				"n_head"					):
          _lowerCAmelCase :       List[Any]	   =     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 :       Union[str, Any]	   =     int(embedding_size / num_head					)
     if hasattr(SCREAMING_SNAKE_CASE		,				"n_layer"					):
          _lowerCAmelCase :       Any	   =     model_config.n_layer
     elif hasattr(SCREAMING_SNAKE_CASE		,				"num_hidden_layers"					):
          _lowerCAmelCase :       Any	   =     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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase :       Any	   =     sparse_model_config(model.config					)
     if model.config.model_type == "bloom":
          _lowerCAmelCase :       str	   =     tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size					)		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
                  .to(model.dtype					)
                  .to(model.device					),
                  torch.empty(int(num_attention_heads * batch_size					)		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
                  .to(model.dtype					)
                  .to(model.device					),
              )
              for _ in range(SCREAMING_SNAKE_CASE					)					)
     else:
          _lowerCAmelCase :       Any	   =     tuple(
              (
                  torch.empty(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
                  .to(model.dtype					)
                  .to(model.device					),
                  torch.empty(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
                  .to(model.dtype					)
                  .to(model.device					),
              )
              for _ in range(SCREAMING_SNAKE_CASE					)					)
     return past_key_values







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   str:

     """simple docstring"""







     _lowerCAmelCase :       Tuple	   =     len(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Tuple	   =     tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE		,				return_tensors="pt"					)
     _lowerCAmelCase :       List[Any]	   =     dummy_input.to(model.device					)
     if model.config.use_cache:
          _lowerCAmelCase :       int	   =     generate_past_key_values(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				1					)
     _lowerCAmelCase :       Optional[int]	   =     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 A__     (			A			):



 """simple docstring"""







 _lowercase  :       List[Any]  			=    ('''_optimized''', '''_default''')

 def __init__(		self			:						Optional[int]  ,    A_			:						List[str]  ,    A_			:						List[str]							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     optimized
      _lowerCAmelCase :       List[str]	   =     default

 def __call__(		self			:						Any  ,    *A_			:						Optional[Any]  ,    **A_			:						Tuple							):






      '''simple docstring'''




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

 def __getattr__(		self			:						List[Any]  ,    A_			:						int							):






      '''simple docstring'''




      return getattr(self._default  ,    A_							)

 def  __magic_name__							(		self			:						Any  ,    A_			:						int  ,    A_			:						List[Any]=None  ,    A_			:						int=None  ,    A_			:						int=None  ,    **A_			:						Optional[int]							):






      '''simple docstring'''




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


 def  __magic_name__							(		self			:						Dict  ,    A_			:						Tuple[Tuple[torch.Tensor]]  ,    A_			:						torch.Tensor							):






      '''simple docstring'''




      return self._default._reorder_cache(A_  ,    A_							)







def 						_snake_case		(     )							->   List[Any]:

     """simple docstring"""







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

     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("--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("--num_return_sequences"		,				type=SCREAMING_SNAKE_CASE		,				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 :       List[Any]	   =     parser.parse_args()

     _lowerCAmelCase :       Optional[Any]	   =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"					)
     _lowerCAmelCase :       Optional[int]	   =     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
     try:
          _lowerCAmelCase :       Tuple	   =     args.model_type.lower()
          _lowerCAmelCase   ,      _lowerCAmelCase :       List[Any]	   =     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 :       Optional[Any]	   =     tokenizer_class.from_pretrained(args.model_name_or_path					)
     if tokenizer.pad_token is None:
          _lowerCAmelCase :       Optional[int]	   =     tokenizer.eos_token
     _lowerCAmelCase :       Optional[Any]	   =     model_class.from_pretrained(args.model_name_or_path					)
     model.to(args.device					)

     if args.fpaa:
          model.half()
     _lowerCAmelCase :       Optional[Any]	   =     getattr(model.config		,				"max_position_embeddings"		,				0					)
     _lowerCAmelCase :       str	   =     adjust_length_to_model(args.length		,				max_sequence_length=SCREAMING_SNAKE_CASE					)
     logger.info(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Optional[int]	   =     args.prompt if args.prompt else input("Model prompt >>> "					)

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

          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               _lowerCAmelCase :       Optional[Any]	   =     {"add_space_before_punct_symbol": True}
          else:
               _lowerCAmelCase :       List[Any]	   =     {}

          _lowerCAmelCase :       int	   =     tokenizer.encode(
              SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_tensors="pt"		,				**SCREAMING_SNAKE_CASE					)
     else:
          _lowerCAmelCase :       Union[str, Any]	   =     args.prefix if args.prefix else args.padding_text
          _lowerCAmelCase :       Dict	   =     tokenizer.encode(prefix + prompt_text		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_tensors="pt"					)
     _lowerCAmelCase :       Union[str, Any]	   =     encoded_prompt.to(args.device					)

     if encoded_prompt.size()[-1] == 0:
          _lowerCAmelCase :       Dict	   =     None
     else:
          _lowerCAmelCase :       Any	   =     encoded_prompt

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

          _lowerCAmelCase :       str	   =     _ModelFallbackWrapper(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       List[str]	   =     model.generate(
         input_ids=SCREAMING_SNAKE_CASE		,				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=SCREAMING_SNAKE_CASE		,				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 :       Union[str, Any]	   =     []

     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE					):
          print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''					)
          _lowerCAmelCase :       int	   =     generated_sequence.tolist()

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

          # Remove all text after the stop token
          _lowerCAmelCase :       List[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
          _lowerCAmelCase :       Any	   =     (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[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()






