


import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


A   :					Union[str, Any]	=      "\\n\n"

A   :					Dict	=      "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

A   :					str	=      "\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"







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


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
                     {
                         "input_texts": datasets.Value("string" ),
                     } )      ,   reference_urls=["https://huggingface.co/docs/transformers/perplexity"]      ,   )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    int = 16      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict=None ):


             '''simple docstring'''



             if device is not None:
                   assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                   if device == "gpu":
                         lowerCamelCase__	:       Optional[Any]					=						"cuda"
             else:
                   lowerCamelCase__	:       str					=						"cuda" if torch.cuda.is_available() else "cpu"

             lowerCamelCase__	:       str					=						AutoModelForCausalLM.from_pretrained(_snake_case )
             lowerCamelCase__	:       List[str]					=						model.to(_snake_case )

             lowerCamelCase__	:       int					=						AutoTokenizer.from_pretrained(_snake_case )

             # if batch_size > 1 (which generally leads to padding being required), and
             # if there is not an already assigned pad_token, assign an existing
             # special token to also be the padding token
             if tokenizer.pad_token is None and batch_size > 1:
                   lowerCamelCase__	:       List[Any]					=						list(tokenizer.special_tokens_map_extended.values() )
                   # check that the model already has at least one special token defined
                   assert (
                       len(_snake_case ) > 0
                   ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                   # assign one of the special tokens to also be the pad token
                   tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )

             if add_start_token:
                   # leave room for <BOS> token to be added:
                   assert (
                       tokenizer.bos_token is not None
                   ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                   lowerCamelCase__	:       Any					=						model.config.max_length - 1
             else:
                   lowerCamelCase__	:       List[str]					=						model.config.max_length

             lowerCamelCase__	:       str					=						tokenizer(
                 _snake_case      ,   add_special_tokens=_snake_case      ,   padding=_snake_case      ,   truncation=_snake_case      ,   max_length=_snake_case      ,   return_tensors="pt"      ,   return_attention_mask=_snake_case      ,   ).to(_snake_case )

             lowerCamelCase__	:       int					=						encodings["input_ids"]
             lowerCamelCase__	:       Any					=						encodings["attention_mask"]

             # check that each input is long enough:
             if add_start_token:
                   assert torch.all(torch.ge(attn_masks.sum(1 )      ,   1 ) ), "Each input text must be at least one token long."
             else:
                   assert torch.all(
                       torch.ge(attn_masks.sum(1 )      ,   2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

             lowerCamelCase__	:       Union[str, Any]					=						[]
             lowerCamelCase__	:       List[str]					=						CrossEntropyLoss(reduction="none" )

             for start_index in logging.tqdm(range(0      ,   len(_snake_case )      ,   _snake_case ) ):
                   lowerCamelCase__	:       int					=						min(start_index + batch_size      ,   len(_snake_case ) )
                   lowerCamelCase__	:       Dict					=						encoded_texts[start_index:end_index]
                   lowerCamelCase__	:       List[Any]					=						attn_masks[start_index:end_index]

                   if add_start_token:
                         lowerCamelCase__	:       List[str]					=						torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(_snake_case )
                         lowerCamelCase__	:       Optional[Any]					=						torch.cat([bos_tokens_tensor, encoded_batch]      ,   dim=1 )
                         lowerCamelCase__	:       Optional[Any]					=						torch.cat(
                             [torch.ones(bos_tokens_tensor.size()      ,   dtype=torch.intaa ).to(_snake_case ), attn_mask]      ,   dim=1 )

                   lowerCamelCase__	:       List[str]					=						encoded_batch

                   with torch.no_grad():
                         lowerCamelCase__	:       int					=						model(_snake_case      ,   attention_mask=_snake_case ).logits

                   lowerCamelCase__	:       Dict					=						out_logits[..., :-1, :].contiguous()
                   lowerCamelCase__	:       Any					=						labels[..., 1:].contiguous()
                   lowerCamelCase__	:       Union[str, Any]					=						attn_mask[..., 1:].contiguous()

                   lowerCamelCase__	:       str					=						torch.expa(
                       (loss_fct(shift_logits.transpose(1      ,   2 )      ,   _snake_case ) * shift_attention_mask_batch).sum(1 )
                       / shift_attention_mask_batch.sum(1 ) )

                   ppls += perplexity_batch.tolist()

             return {"perplexities": ppls, "mean_perplexity": np.mean(_snake_case )}






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





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


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


