
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	  =					logging.getLogger(__name__)

_A	  =					int(10_000)  # Hardcoded max length to avoid infinite loop

_A	  =					{
    '''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	  =					'''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__				(						a__  :  Union[str, Any]     )		->	Any:
       np.random.seed(args.seed     )
       torch.manual_seed(args.seed     )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )




def 				lowerCamelCase__				(						a__  :  Optional[int]	,      a__  :  Union[str, Any]	,      a__  :  Optional[int]	,      a__  :  Union[str, Any]     )		->	Optional[int]:
       if args.temperature > 0.7:
              logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""     )

       UpperCamelCase_							       =       tokenizer.encode(a__	,      add_special_tokens=a__     )
       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__				(						a__  :  int	,      a__  :  Union[str, Any]	,      a__  :  int	,      a__  :  str     )		->	List[str]:
       # kwargs = {"language": None, "mask_token_id": None}

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

              UpperCamelCase_							       =       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__				(						a__  :  Any	,      a__  :  Union[str, Any]	,      a__  :  Tuple	,      a__  :  List[str]     )		->	Dict:
       UpperCamelCase_							       =       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       UpperCamelCase_							       =       prefix + prompt_text
       return prompt_text




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,      a__  :  Optional[int]	,      a__  :  List[Any]	,      a__  :  Dict     )		->	Optional[int]:
       UpperCamelCase_							       =       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       UpperCamelCase_							       =       prefix + prompt_text
       return prompt_text


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




def 				lowerCamelCase__				(						a__  :  int	,      a__  :  Union[str, Any]     )		->	Optional[Any]:
       if length < 0 and max_sequence_length > 0:
              UpperCamelCase_							       =       max_sequence_length
       elif 0 < max_sequence_length < length:
              UpperCamelCase_							       =       max_sequence_length  # No generation bigger than model size
       elif length < 0:
              UpperCamelCase_							       =       MAX_LENGTH  # avoid infinite loop
       return length




def 				lowerCamelCase__				(						a__  :  Tuple     )		->	List[str]:
       UpperCamelCase_							       =       None
       if hasattr(a__	,      """hidden_size"""     ):
              UpperCamelCase_							       =       model_config.hidden_size
       elif hasattr(a__	,      """n_embed"""     ):
              UpperCamelCase_							       =       model_config.n_embed
       elif hasattr(a__	,      """n_embd"""     ):
              UpperCamelCase_							       =       model_config.n_embd

       UpperCamelCase_							       =       None
       if hasattr(a__	,      """num_attention_heads"""     ):
              UpperCamelCase_							       =       model_config.num_attention_heads
       elif hasattr(a__	,      """n_head"""     ):
              UpperCamelCase_							       =       model_config.n_head

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

       UpperCamelCase_							       =       int(embedding_size / num_head     )
       if hasattr(a__	,      """n_layer"""     ):
              UpperCamelCase_							       =       model_config.n_layer
       elif hasattr(a__	,      """num_hidden_layers"""     ):
              UpperCamelCase_							       =       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__				(						a__  :  str	,      a__  :  str	,      a__  :  Union[str, Any]     )		->	List[str]:
       UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       sparse_model_config(model.config     )
       if model.config.model_type == "bloom":
              UpperCamelCase_							       =       tuple(
                  (
                      torch.empty(int(num_attention_heads * batch_size     )	,      a__	,      a__     )
                      .to(model.dtype     )
                      .to(model.device     ),
                      torch.empty(int(num_attention_heads * batch_size     )	,      a__	,      a__     )
                      .to(model.dtype     )
                      .to(model.device     ),
                  )
                  for _ in range(a__     )     )
       else:
              UpperCamelCase_							       =       tuple(
                  (
                      torch.empty(a__	,      a__	,      a__	,      a__     )
                      .to(model.dtype     )
                      .to(model.device     ),
                      torch.empty(a__	,      a__	,      a__	,      a__     )
                      .to(model.dtype     )
                      .to(model.device     ),
                  )
                  for _ in range(a__     )     )
       return past_key_values




def 				lowerCamelCase__				(						a__  :  Optional[Any]	,      a__  :  str	,      a__  :  List[str]     )		->	Tuple:
       UpperCamelCase_							       =       len(a__     )
       UpperCamelCase_							       =       tokenizer.batch_encode_plus(a__	,      return_tensors="""pt"""     )
       UpperCamelCase_							       =       dummy_input.to(model.device     )
       if model.config.use_cache:
              UpperCamelCase_							       =       generate_past_key_values(a__	,      a__	,      1     )
       UpperCamelCase_							       =       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_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				str         =    ("""_optimized""", """_default""")

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       optimized
        UpperCamelCase_							       =       default

 def __call__(      self     ,	*__UpperCamelCase     ,	**__UpperCamelCase     ):




        """simple docstring"""


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

 def __getattr__(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        return getattr(self._default     ,	__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	**__UpperCamelCase     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        return self._default._reorder_cache(__UpperCamelCase     ,	__UpperCamelCase     )




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

       parser.add_argument("""--prompt"""	,      type=a__	,      default=""""""     )
       parser.add_argument("""--length"""	,      type=a__	,      default=20     )
       parser.add_argument("""--stop_token"""	,      type=a__	,      default=a__	,      help="""Token at which text generation is stopped"""     )

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

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

       parser.add_argument("""--seed"""	,      type=a__	,      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=a__	,      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"""     )
       UpperCamelCase_							       =       parser.parse_args()

       UpperCamelCase_							       =       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
       UpperCamelCase_							       =       0 if args.no_cuda else torch.cuda.device_count()

       logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''     )

       set_seed(a__     )

       # Initialize the model and tokenizer
       try:
              UpperCamelCase_							       =       args.model_type.lower()
              UpperCamelCase_       ,				UpperCamelCase_							       =       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 :)"""     )

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

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

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

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

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

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

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

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

              UpperCamelCase_							       =       _ModelFallbackWrapper(a__	,      a__     )

       UpperCamelCase_							       =       model.generate(
           input_ids=a__	,      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=a__	,      num_return_sequences=args.num_return_sequences	,      )

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

       UpperCamelCase_							       =       []

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

              # Decode text
              UpperCamelCase_							       =       tokenizer.decode(a__	,      clean_up_tokenization_spaces=a__     )

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

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

              generated_sequences.append(a__     )
              print(a__     )

       return generated_sequences


if __name__ == "__main__":
      main()





def 				lowerCamelCase__				(						a__  :  list     )		->	list:
       UpperCamelCase_							       =       1.3
       UpperCamelCase_							       =       len(a__     )
       UpperCamelCase_							       =       False

       while not completed:
              # Update the gap value for a next comb
              UpperCamelCase_							       =       int(gap / shrink_factor     )
              if gap <= 1:
                     UpperCamelCase_							       =       True

              UpperCamelCase_							       =       0
              while index + gap < len(a__     ):
                     if data[index] > data[index + gap]:
                            # Swap values
                            UpperCamelCase_       ,				UpperCamelCase_							       =       data[index + gap], data[index]
                            UpperCamelCase_							       =       False
                     index += 1

       return data


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      _A	  =					input('''Enter numbers separated by a comma:\n''').strip()
      _A	  =					[int(item) for item in user_input.split(''',''')]
      print(comb_sort(unsorted))



