



"""simple docstring"""






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

import datasets
from datasets import logging


__snake_case							:     Tuple   =					'\\n\n'

__snake_case							:     Optional[int]   =					'\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'

__snake_case							:     Dict   =					'\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     A__     (						datasets.Metric							):




 '''simple docstring'''





 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Union[str, Any]:

    """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 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	int = 16 ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=None)       -> List[Any]:

    """simple docstring"""

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

    __lowerCAmelCase   :						Union[str, Any]				= AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Tuple				= model.to(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						str				= AutoTokenizer.from_pretrained(_SCREAMING_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   :						int				= list(tokenizer.special_tokens_map_extended.values())
       # check that the model already has at least one special token defined
       assert (
           len(_SCREAMING_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   :						List[str]				= model.config.max_length - 1
    else:
       __lowerCAmelCase   :						Union[str, Any]				= model.config.max_length

    __lowerCAmelCase   :						List[Any]				= tokenizer(
        _SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_tensors="pt" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    ).to(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						Optional[Any]				= encodings["input_ids"]
    __lowerCAmelCase   :						int				= 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   :						List[str]				= []
    __lowerCAmelCase   :						Tuple				= CrossEntropyLoss(reduction="none")

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

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

       __lowerCAmelCase   :						Tuple				= encoded_batch

       with torch.no_grad():
          __lowerCAmelCase   :						int				= model(_SCREAMING_SNAKE_CASE ,    attention_mask=_SCREAMING_SNAKE_CASE).logits

       __lowerCAmelCase   :						Dict				= out_logits[..., :-1, :].contiguous()
       __lowerCAmelCase   :						Tuple				= labels[..., 1:].contiguous()
       __lowerCAmelCase   :						str				= attn_mask[..., 1:].contiguous()

       __lowerCAmelCase   :						List[str]				= torch.expa(
           (loss_fct(shift_logits.transpose(1 ,    2) ,    _SCREAMING_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(_SCREAMING_SNAKE_CASE)}




"""simple docstring"""






import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__snake_case							:     Tuple   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Optional[Any]				= set()
			__lowerCAmelCase   :						List[Any]				= []

			def parse_line(__snake_case			):
						for line in fp:
									if isinstance(__snake_case      ,__snake_case			):
												__lowerCAmelCase   :						Tuple				= line.decode("UTF-8"			)
									if "warnings summary (final)" in line:
												continue
									# This means we are outside the body of a warning
									elif not line.startswith(" "			):
												# process a single warning and move it to `selected_warnings`.
												if len(__snake_case			) > 0:
															__lowerCAmelCase   :						List[Any]				= "\n".join(__snake_case			)
															# Only keep the warnings specified in `targets`
															if any(F""": {x}: """ in warning for x in targets			):
																		selected_warnings.add(__snake_case			)
															buffer.clear()
												continue
									else:
												__lowerCAmelCase   :						List[str]				= line.strip()
												buffer.append(__snake_case			)

			if from_gh:
						for filename in os.listdir(__snake_case			):
									__lowerCAmelCase   :						List[str]				= os.path.join(__snake_case      ,__snake_case			)
									if not os.path.isdir(__snake_case			):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(__snake_case			) as fp:
															parse_line(__snake_case			)
			else:
						try:
									with zipfile.ZipFile(__snake_case			) as z:
												for filename in z.namelist():
															if not os.path.isdir(__snake_case			):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(__snake_case			) as fp:
																					parse_line(__snake_case			)
						except Exception:
									logger.warning(
									    F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

			return selected_warnings


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Any				= set()

			__lowerCAmelCase   :						str				= [os.path.join(__snake_case      ,__snake_case			) for p in os.listdir(__snake_case			) if (p.endswith(".zip"			) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(__snake_case      ,__snake_case			)			)

			return selected_warnings


if __name__ == "__main__":


	def     _lowercase       (						__snake_case			)		->     Optional[Any]:
				return values.split(","			)

	__snake_case							:     Optional[int]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
	parser.add_argument(
	    '--output_dir',
	    type=str,
	    required=True,
	    help='Where to store the downloaded artifacts and other result files.',
	)
	parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
	# optional parameters
	parser.add_argument(
	    '--targets',
	    default='DeprecationWarning,UserWarning,FutureWarning',
	    type=list_str,
	    help='Comma-separated list of target warning(s) which we want to extract.',
	)
	parser.add_argument(
	    '--from_gh',
	    action='store_true',
	    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
	)

	__snake_case							:     Tuple   =					parser.parse_args()

	__snake_case							:     Any   =					args.from_gh
	if from_gh:
		# The artifacts have to be downloaded using `actions/download-artifact@v3`
		pass
	else:
		os.makedirs(args.output_dir, exist_ok=True)

		# get download links
		__snake_case							:     str   =					get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
			json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		# download artifacts
		for idx, (name, url) in enumerate(artifacts.items()):
			print(name)
			print(url)
			print('=' * 80)
			download_artifact(name, url, args.output_dir, args.token)
			# Be gentle to GitHub
			time.sleep(1)

    # extract warnings from artifacts
	__snake_case							:     Tuple   =					extract_warnings(args.output_dir, args.targets)
	__snake_case							:     List[str]   =					sorted(selected_warnings)
	with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
		json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)