



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

import datasets
from datasets import logging


__snake_case     :Union[str, Any]			       ='\\n\n'

__snake_case     :List[str]			       ='\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     :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        lowerCAmelCase__					(  datasets.Metric       ):


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Dict:
											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      :  Dict						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  int = 16						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Dict=None       )		->     Any:
											if device is not None:
																		assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
																		if device == "gpu":
																									A      						=    'cuda'
											else:
																		A      						=    'cuda' if torch.cuda.is_available() else 'cpu'

											A      						=    AutoModelForCausalLM.from_pretrained(__UpperCamelCase       )
											A      						=    model.to(__UpperCamelCase       )

											A      						=    AutoTokenizer.from_pretrained(__UpperCamelCase       )

											# 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:
																		A      						=    list(tokenizer.special_tokens_map_extended.values()       )
																		# check that the model already has at least one special token defined
																		assert (
																		    len(__UpperCamelCase       ) > 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"
																		A      						=    model.config.max_length - 1
											else:
																		A      						=    model.config.max_length

											A      						=    tokenizer(
											    __UpperCamelCase						, add_special_tokens=__UpperCamelCase						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, max_length=__UpperCamelCase						, return_tensors='pt'						, return_attention_mask=__UpperCamelCase						, ).to(__UpperCamelCase       )

											A      						=    encodings['input_ids']
											A      						=    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."

											A      						=    []
											A      						=    CrossEntropyLoss(reduction='none'       )

											for start_index in logging.tqdm(range(0						, len(__UpperCamelCase       )						, __UpperCamelCase       )       ):
																		A      						=    min(start_index + batch_size						, len(__UpperCamelCase       )       )
																		A      						=    encoded_texts[start_index:end_index]
																		A      						=    attn_masks[start_index:end_index]

																		if add_start_token:
																									A      						=    torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(__UpperCamelCase       )
																									A      						=    torch.cat([bos_tokens_tensor, encoded_batch]						, dim=1       )
																									A      						=    torch.cat(
																									    [torch.ones(bos_tokens_tensor.size()						, dtype=torch.intaa       ).to(__UpperCamelCase       ), attn_mask]						, dim=1       )

																		A      						=    encoded_batch

																		with torch.no_grad():
																									A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase       ).logits

																		A      						=    out_logits[..., :-1, :].contiguous()
																		A      						=    labels[..., 1:].contiguous()
																		A      						=    attn_mask[..., 1:].contiguous()

																		A      						=    torch.expa(
																		    (loss_fct(shift_logits.transpose(1						, 2       )						, __UpperCamelCase       ) * shift_attention_mask_batch).sum(1       )
																		    / shift_attention_mask_batch.sum(1       )       )

																		ppls += perplexity_batch.tolist()

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




import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				metadata={
				        'help': (
				            'The maximum length of an answer that can be generated. This is needed because the start '
				            'and end predictions are not conditioned on one another.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											A      						=    os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir						, f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''						, )

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A      						=    cached_features_file + '.lock'
											with FileLock(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									logger.info(
																									    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						, time.time() - start       )

																									if self.dataset is None or self.examples is None:
																																logger.warning(
																																    f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																																    ' future run'       )
																		else:
																									if mode == Split.dev:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, max_seq_length=args.max_seq_length						, doc_stride=args.doc_stride						, max_query_length=args.max_query_length						, is_training=mode == Split.train						, threads=args.threads						, return_dataset=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																									logger.info(
																									    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )


				def __len__(							self      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											    'token_type_ids': token_type_ids,
											}

											if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																		del inputs["token_type_ids"]

											if self.args.model_type in ["xlnet", "xlm"]:
																		inputs.update({'cls_index': cls_index, 'p_mask': p_mask}       )
																		if self.args.version_2_with_negative:
																									inputs.update({'is_impossible': is_impossible}       )
																		if self.is_language_sensitive:
																									inputs.update({'langs': (torch.ones(input_ids.shape						, dtype=torch.intaa       ) * self.args.lang_id)}       )

											if self.mode == Split.train:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs