






"""simple docstring"""



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__	:    Optional[int]			 = '\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       ):


			def      UpperCAmelCase__						(				self					:  List[str] ):
										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      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  int = 16       ,	snake_case__					:  bool = True       ,	snake_case__					:  Dict=None ):
										if device is not None:
																	assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
																	if device == "gpu":
																								lowerCamelCase_	:							List[str]									="cuda"
										else:
																	lowerCamelCase_	:							Union[str, Any]									="cuda" if torch.cuda.is_available() else "cpu"

										lowerCamelCase_	:							List[str]									=AutoModelForCausalLM.from_pretrained(snake_case__ )
										lowerCamelCase_	:							Optional[int]									=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_	:							Optional[int]									=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_	:							int									=model.config.max_length - 1
										else:
																	lowerCamelCase_	:							List[str]									=model.config.max_length

										lowerCamelCase_	:							Dict									=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_	:							List[str]									=encodings["input_ids"]
										lowerCamelCase_	:							Union[str, 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_	:							int									=[]
										lowerCamelCase_	:							Optional[int]									=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_	:							Any									=encoded_texts[start_index:end_index]
																	lowerCamelCase_	:							Tuple									=attn_masks[start_index:end_index]

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

																	lowerCamelCase_	:							Tuple									=encoded_batch

																	with torch.no_grad():
																								lowerCamelCase_	:							Any									=model(snake_case__       ,	attention_mask=snake_case__ ).logits

																	lowerCamelCase_	:							List[Any]									=out_logits[..., :-1, :].contiguous()
																	lowerCamelCase_	:							List[str]									=labels[..., 1:].contiguous()
																	lowerCamelCase_	:							Optional[Any]									=attn_mask[..., 1:].contiguous()

																	lowerCamelCase_	:							int									=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__ )}











"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A__	:    List[str]			 = datasets.utils.logging.get_logger(__name__)







@dataclass
class        lowercase__      (							datasets.BuilderConfig       ):
			_UpperCAmelCase		:int             =			10000
			_UpperCAmelCase		:Optional[List[str]]             =			None
			_UpperCAmelCase		:Optional[datasets.Features]             =			None







class        lowercase__      (							datasets.ArrowBasedBuilder       ):
			_UpperCAmelCase		:Optional[int]             =			ParquetConfig


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return datasets.DatasetInfo(features=self.config.features )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any] ):
										if not self.config.data_files:
																	raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
										lowerCamelCase_	:							str									=dl_manager.download_and_extract(self.config.data_files )
										if isinstance(snake_case__       ,	(str, list, tuple) ):
																	lowerCamelCase_	:							Dict									=data_files
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							List[Any]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							Any									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={"files": files} )]
										lowerCamelCase_	:							Optional[int]									=[]
										for split_name, files in data_files.items():
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Optional[int]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							int									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(snake_case__ ):
																															with open(snake_case__       ,	"rb" ) as f:
																																						lowerCamelCase_	:							List[Any]									=datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
																															break
																	splits.append(datasets.SplitGenerator(name=snake_case__       ,	gen_kwargs={"files": files} ) )
										return splits


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pa.Table ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowerCamelCase_	:							List[str]									=table_cast(snake_case__       ,	self.info.features.arrow_schema )
										return pa_table


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema ) != sorted(self.config.columns ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
										for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
																	with open(snake_case__       ,	"rb" ) as f:
																								lowerCamelCase_	:							List[str]									=pq.ParquetFile(snake_case__ )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,	columns=self.config.columns ) ):
																																						lowerCamelCase_	:							Union[str, Any]									=pa.Table.from_batches([record_batch] )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__ )
																								except ValueError as e:
																															logger.error(F"""Failed to read file '{file}' with error {type(snake_case__ )}: {e}""" )
																															raise



