






'''simple docstring'''





import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__A				:				Dict					=		get_logger(__name__)






def  lowerCamelCase_  (     lowercase__):
 if version.parse(lowercase__) < version.parse(lowercase__):
  if "dev" in min_version:
   lowerCamelCase__	=  (
       "This example requires a source install from HuggingFace diffusers (see "
       "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
   )
  else:
   lowerCamelCase__	=  F'''This example requires a minimum version of {min_version},'''
  error_message += F''' but the version found is {__version__}.\n'''
  raise ImportError(lowercase__)









'''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()

