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(10000)  # 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
(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 						UpperCAmelCase							(		lowerCAmelCase_							)						->						Optional[Any]:
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Union[str, Any]:
     if args.temperature > 0.7:
          logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''							)

     a	:     Tuple						      =    tokenizer.encode(UpperCAmelCase__    ,   add_special_tokens=UpperCAmelCase__							)
     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 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[int]:
     # kwargs = {"language": None, "mask_token_id": None}

     # Set the language
     a	:     Union[str, Any]						      =    hasattr(model.config    ,   '''use_lang_emb'''							) and model.config.use_lang_emb
     if hasattr(model.config    ,   '''lang2id'''							) and use_lang_emb:
          a	:     Dict						      =    model.config.langaid.keys()
          if args.xlm_language in available_languages:
               a	:     int						      =    args.xlm_language
          else:
               a	:     str						      =    None
               while language not in available_languages:
                    a	:     Tuple						      =    input('''Using XLM. Select language in ''' + str(list(UpperCAmelCase__							)							) + ''' >>> '''							)

          a	:     List[str]						      =    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 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     a	:     List[Any]						      =    prefix + prompt_text
     return prompt_text







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						str:
     a	:     Tuple						      =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     a	:     Optional[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 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     if length < 0 and max_sequence_length > 0:
          a	:     str						      =    max_sequence_length
     elif 0 < max_sequence_length < length:
          a	:     str						      =    max_sequence_length  # No generation bigger than model size
     elif length < 0:
          a	:     Optional[Any]						      =    MAX_LENGTH  # avoid infinite loop
     return length







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						List[str]:
     a	:     Any						      =    None
     if hasattr(UpperCAmelCase__    ,   '''hidden_size'''							):
          a	:     str						      =    model_config.hidden_size
     elif hasattr(UpperCAmelCase__    ,   '''n_embed'''							):
          a	:     str						      =    model_config.n_embed
     elif hasattr(UpperCAmelCase__    ,   '''n_embd'''							):
          a	:     Tuple						      =    model_config.n_embd

     a	:     Optional[Any]						      =    None
     if hasattr(UpperCAmelCase__    ,   '''num_attention_heads'''							):
          a	:     int						      =    model_config.num_attention_heads
     elif hasattr(UpperCAmelCase__    ,   '''n_head'''							):
          a	:     str						      =    model_config.n_head

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

     a	:     Tuple						      =    int(embedding_size / num_head							)
     if hasattr(UpperCAmelCase__    ,   '''n_layer'''							):
          a	:     Any						      =    model_config.n_layer
     elif hasattr(UpperCAmelCase__    ,   '''num_hidden_layers'''							):
          a	:     int						      =    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 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						str:
     a      ,				a      ,				a	:     Optional[int]						      =    sparse_model_config(model.config							)
     if model.config.model_type == "bloom":
          a	:     int						      =    tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size							)    ,   UpperCAmelCase__    ,   UpperCAmelCase__							)
                  .to(model.dtype							)
                  .to(model.device							),
                  torch.empty(int(num_attention_heads * batch_size							)    ,   UpperCAmelCase__    ,   UpperCAmelCase__							)
                  .to(model.dtype							)
                  .to(model.device							),
              )
              for _ in range(UpperCAmelCase__							)							)
     else:
          a	:     Any						      =    tuple(
              (
                  torch.empty(UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__							)
                  .to(model.dtype							)
                  .to(model.device							),
                  torch.empty(UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__							)
                  .to(model.dtype							)
                  .to(model.device							),
              )
              for _ in range(UpperCAmelCase__							)							)
     return past_key_values







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Tuple:
     a	:     Union[str, Any]						      =    len(UpperCAmelCase__							)
     a	:     Optional[int]						      =    tokenizer.batch_encode_plus(UpperCAmelCase__    ,   return_tensors='''pt'''							)
     a	:     int						      =    dummy_input.to(model.device							)
     if model.config.use_cache:
          a	:     int						      =    generate_past_key_values(UpperCAmelCase__    ,   UpperCAmelCase__    ,   1							)
     a	:     List[Any]						      =    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 				_SCREAMING_SNAKE_CASE( __lowerCamelCase						):
   UpperCamelCase_					:    Any			      = ('_optimized', '_default')
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Dict:
        a	:     int						      =    optimized
        a	:     Dict						      =    default
   def __call__(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->List[str]:
        if kwargs["past_key_values"] is None and self._default.config.use_cache:
             a	:     Any						      =    generate_past_key_values(self._default				,  kwargs['''input_ids'''].shape[0]				,  0     )
        kwargs.pop('''position_ids'''				,  _lowercase     )
        for k in list(kwargs.keys()     ):
             if kwargs[k] is None or isinstance(kwargs[k]				,  _lowercase     ):
                  kwargs.pop(_lowercase     )
        a	:     int						      =    self._optimized(**_lowercase     )
        a	:     Union[str, Any]						      =    outputs[0]
        a	:     List[str]						      =    outputs[1]
        a	:     str						      =    CausalLMOutputWithPast(
            loss=_lowercase				,  logits=_lowercase				,  past_key_values=_lowercase				,  hidden_states=_lowercase				,  attentions=_lowercase				,  )
        return fixed_output
   def __getattr__(     self				,  lowerCAmelCase_     )						->int:
        return getattr(self._default				,  _lowercase     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  **lowerCAmelCase_     )						->Union[str, Any]:
        return self._default.prepare_inputs_for_generation(
            _lowercase				,  past_key_values=_lowercase				,  inputs_embeds=_lowercase				,  use_cache=_lowercase				,  **_lowercase     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Any:
        return self._default._reorder_cache(_lowercase				,  _lowercase     )







def 						UpperCAmelCase							(		)						->						str:
     a	:     List[str]						      =    argparse.ArgumentParser()
     parser.add_argument(
         '''--model_type'''    ,   default=UpperCAmelCase__    ,   type=UpperCAmelCase__    ,   required=UpperCAmelCase__    ,   help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()							)    ,   )
     parser.add_argument(
         '''--model_name_or_path'''    ,   default=UpperCAmelCase__    ,   type=UpperCAmelCase__    ,   required=UpperCAmelCase__    ,   help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()							)    ,   )

     parser.add_argument('''--prompt'''    ,   type=UpperCAmelCase__    ,   default=''''''							)
     parser.add_argument('''--length'''    ,   type=UpperCAmelCase__    ,   default=2_0							)
     parser.add_argument('''--stop_token'''    ,   type=UpperCAmelCase__    ,   default=UpperCAmelCase__    ,   help='''Token at which text generation is stopped'''							)

     parser.add_argument(
         '''--temperature'''    ,   type=UpperCAmelCase__    ,   default=1.0    ,   help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''    ,   )
     parser.add_argument(
         '''--repetition_penalty'''    ,   type=UpperCAmelCase__    ,   default=1.0    ,   help='''primarily useful for CTRL model; in that case, use 1.2'''							)
     parser.add_argument('''--k'''    ,   type=UpperCAmelCase__    ,   default=0							)
     parser.add_argument('''--p'''    ,   type=UpperCAmelCase__    ,   default=0.9							)

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

     parser.add_argument('''--seed'''    ,   type=UpperCAmelCase__    ,   default=4_2    ,   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=UpperCAmelCase__    ,   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'''							)
     a	:     Union[str, Any]						      =    parser.parse_args()

     a	:     Union[str, Any]						      =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
     a	:     Dict						      =    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(UpperCAmelCase__							)

     # Initialize the model and tokenizer
     try:
          a	:     int						      =    args.model_type.lower()
          a      ,				a	:     Tuple						      =    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 :)'''							)

     a	:     List[Any]						      =    tokenizer_class.from_pretrained(args.model_name_or_path							)
     if tokenizer.pad_token is None:
          a	:     Any						      =    tokenizer.eos_token
     a	:     Optional[Any]						      =    model_class.from_pretrained(args.model_name_or_path							)
     model.to(args.device							)

     if args.fpaa:
          model.half()
     a	:     Dict						      =    getattr(model.config    ,   '''max_position_embeddings'''    ,   0							)
     a	:     Union[str, Any]						      =    adjust_length_to_model(args.length    ,   max_sequence_length=UpperCAmelCase__							)
     logger.info(UpperCAmelCase__							)

     a	:     Any						      =    args.prompt if args.prompt else input('''Model prompt >>> '''							)

     # Different models need different input formatting and/or extra arguments
     a	:     int						      =    args.model_type in PREPROCESSING_FUNCTIONS.keys()
     if requires_preprocessing:
          a	:     Union[str, Any]						      =    PREPROCESSING_FUNCTIONS.get(args.model_type							)
          a	:     Union[str, Any]						      =    prepare_input(UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__    ,   UpperCAmelCase__							)

          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               a	:     Any						      =    {'''add_space_before_punct_symbol''': True}
          else:
               a	:     Optional[int]						      =    {}

          a	:     Any						      =    tokenizer.encode(
              UpperCAmelCase__    ,   add_special_tokens=UpperCAmelCase__    ,   return_tensors='''pt'''    ,   **UpperCAmelCase__							)
     else:
          a	:     Any						      =    args.prefix if args.prefix else args.padding_text
          a	:     int						      =    tokenizer.encode(prefix + prompt_text    ,   add_special_tokens=UpperCAmelCase__    ,   return_tensors='''pt'''							)
     a	:     List[Any]						      =    encoded_prompt.to(args.device							)

     if encoded_prompt.size()[-1] == 0:
          a	:     List[Any]						      =    None
     else:
          a	:     Tuple						      =    encoded_prompt

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

          a	:     Optional[Any]						      =    _ModelFallbackWrapper(UpperCAmelCase__    ,   UpperCAmelCase__							)

     a	:     Optional[Any]						      =    model.generate(
         input_ids=UpperCAmelCase__    ,   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=UpperCAmelCase__    ,   num_return_sequences=args.num_return_sequences    ,   )

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

     a	:     List[Any]						      =    []

     for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase__							):
          print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""							)
          a	:     str						      =    generated_sequence.tolist()

          # Decode text
          a	:     Union[str, Any]						      =    tokenizer.decode(UpperCAmelCase__    ,   clean_up_tokenization_spaces=UpperCAmelCase__							)

          # Remove all text after the stop token
          a	:     List[str]						      =    text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          a	:     int						      =    (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0]    ,   clean_up_tokenization_spaces=UpperCAmelCase__							)							) :]
          )

          generated_sequences.append(UpperCAmelCase__							)
          print(UpperCAmelCase__							)

     return generated_sequences


if __name__ == "__main__":
  main()
"""simple docstring"""







def 	a__   (		UpperCAmelCase__							:      list[int]      )->						int:
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__      )      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

