


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,
)
__lowerCamelCase   	=  logging.getLogger(__name__)

__lowerCamelCase   	=  int(10000)  # Hardcoded max length to avoid infinite loop

__lowerCamelCase   	=  {
    '''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
__lowerCamelCase   	=  '''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 			_a      (    __UpperCamelCase			):
     np.random.seed(args.seed			)
     torch.manual_seed(args.seed			)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed			)



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

     a_     :  Any				      =     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     # kwargs = {"language": None, "mask_token_id": None}

     # Set the language
     a_     :  str				      =     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_     :  Union[str, Any]				      =     args.xlm_language
          else:
               a_     :  List[str]				      =     None
               while language not in available_languages:
                    a_     :  Optional[Any]				      =     input("""Using XLM. Select language in """ + str(list(__UpperCamelCase			)			) + """ >>> """			)

          a_     :  int				      =     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     a_     :  str				      =     prefix + prompt_text
     return prompt_text



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  int				      =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     a_     :  Dict				      =     prefix + prompt_text
     return prompt_text


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



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     if length < 0 and max_sequence_length > 0:
          a_     :  Tuple				      =     max_sequence_length
     elif 0 < max_sequence_length < length:
          a_     :  Optional[int]				      =     max_sequence_length  # No generation bigger than model size
     elif length < 0:
          a_     :  Tuple				      =     MAX_LENGTH  # avoid infinite loop
     return length



def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     None
     if hasattr(__UpperCamelCase	,	"""hidden_size"""			):
          a_     :  Optional[int]				      =     model_config.hidden_size
     elif hasattr(__UpperCamelCase	,	"""n_embed"""			):
          a_     :  Optional[Any]				      =     model_config.n_embed
     elif hasattr(__UpperCamelCase	,	"""n_embd"""			):
          a_     :  str				      =     model_config.n_embd

     a_     :  Dict				      =     None
     if hasattr(__UpperCamelCase	,	"""num_attention_heads"""			):
          a_     :  int				      =     model_config.num_attention_heads
     elif hasattr(__UpperCamelCase	,	"""n_head"""			):
          a_     :  Optional[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"""			)

     a_     :  List[Any]				      =     int(embedding_size / num_head			)
     if hasattr(__UpperCamelCase	,	"""n_layer"""			):
          a_     :  str				      =     model_config.n_layer
     elif hasattr(__UpperCamelCase	,	"""num_hidden_layers"""			):
          a_     :  str				      =     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_      ,      a_     :  int				      =     sparse_model_config(model.config			)
     if model.config.model_type == "bloom":
          a_     :  Tuple				      =     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_     :  Optional[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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[int]				      =     len(__UpperCamelCase			)
     a_     :  List[str]				      =     tokenizer.batch_encode_plus(__UpperCamelCase	,	return_tensors="""pt"""			)
     a_     :  Optional[int]				      =     dummy_input.to(model.device			)
     if model.config.use_cache:
          a_     :  str				      =     generate_past_key_values(__UpperCamelCase	,	__UpperCamelCase	,	1			)
     a_     :  Tuple				      =     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__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  str				     =  ("""_optimized""", """_default""")

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Any     ):
            a_     :  List[str]				      =     optimized
            a_     :  Optional[Any]				      =     default

       def __call__( self   :  Any   ,				*lowerCamelCase_   :  Optional[int]   ,				**lowerCamelCase_   :  str     ):
            if kwargs["past_key_values"] is None and self._default.config.use_cache:
                 a_     :  Optional[int]				      =     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_     )
            a_     :  Dict				      =     self._optimized(**lowerCamelCase_     )
            a_     :  int				      =     outputs[0]
            a_     :  str				      =     outputs[1]
            a_     :  Optional[Any]				      =     CausalLMOutputWithPast(
                loss=lowerCamelCase_   ,				logits=lowerCamelCase_   ,				past_key_values=lowerCamelCase_   ,				hidden_states=lowerCamelCase_   ,				attentions=lowerCamelCase_   ,				)
            return fixed_output

       def __getattr__( self   :  int   ,				lowerCamelCase_   :  Optional[Any]     ):
            return getattr(self._default   ,				lowerCamelCase_     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  List[Any]=None   ,				lowerCamelCase_   :  Optional[Any]=None   ,				lowerCamelCase_   :  str=None   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            return self._default.prepare_inputs_for_generation(
                lowerCamelCase_   ,				past_key_values=lowerCamelCase_   ,				inputs_embeds=lowerCamelCase_   ,				use_cache=lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Tuple[Tuple[torch.Tensor]]   ,				lowerCamelCase_   :  torch.Tensor     ):
            return self._default._reorder_cache(lowerCamelCase_   ,				lowerCamelCase_     )



def 			_a      (    ):
     a_     :  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_     :  int				      =     parser.parse_args()

     a_     :  Optional[Any]				      =     torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
     a_     :  str				      =     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_     :  Optional[Any]				      =     args.model_type.lower()
          a_      ,      a_     :  Optional[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 :)"""			)

     a_     :  Tuple				      =     tokenizer_class.from_pretrained(args.model_name_or_path			)
     if tokenizer.pad_token is None:
          a_     :  Union[str, Any]				      =     tokenizer.eos_token
     a_     :  List[str]				      =     model_class.from_pretrained(args.model_name_or_path			)
     model.to(args.device			)

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

     a_     :  Union[str, Any]				      =     args.prompt if args.prompt else input("""Model prompt >>> """			)

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

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

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

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

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

          a_     :  Dict				      =     _ModelFallbackWrapper(__UpperCamelCase	,	__UpperCamelCase			)

     a_     :  Union[str, 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_     :  int				      =     []

     for generated_sequence_idx, generated_sequence in enumerate(__UpperCamelCase			):
          print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''			)
          a_     :  Optional[int]				      =     generated_sequence.tolist()

          # Decode text
          a_     :  List[str]				      =     tokenizer.decode(__UpperCamelCase	,	clean_up_tokenization_spaces=__UpperCamelCase			)

          # Remove all text after the stop token
          a_     :  Dict				      =     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_     :  Optional[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'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,  _KWARGS_DESCRIPTION				)
class 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      return datasets.MetricInfo(
          description=_DESCRIPTION	,     citation=_CITATION	,     inputs_description=_KWARGS_DESCRIPTION	,     features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32'			)			),
                  'references': datasets.Sequence(datasets.Value('int32'			)			),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'			),
                  'references': datasets.Value('int32'			),
              }			)	,     reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']	,     )




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




