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

import datasets
from datasets import logging


lowercase__		:Any			  =   "\\n\n"

lowercase__		:Any			  =   "\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"

lowercase__		:Any			  =   "\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 			A__				(    self):
												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 			A__				(    self       ,A__       ,A__       ,A__ = 1_6       ,A__ = True       ,A__=None):
												if device is not None:
																	assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
																	if device == "gpu":
																						lowercase						     =						'''cuda'''
												else:
																	lowercase						     =						'''cuda''' if torch.cuda.is_available() else '''cpu'''

												lowercase						     =						AutoModelForCausalLM.from_pretrained(A__)
												lowercase						     =						model.to(A__)

												lowercase						     =						AutoTokenizer.from_pretrained(A__)

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

												lowercase						     =						tokenizer(
												    A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,return_tensors='''pt'''       ,return_attention_mask=A__       ,).to(A__)

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

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

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

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

																	lowercase						     =						encoded_batch

																	with torch.no_grad():
																						lowercase						     =						model(A__       ,attention_mask=A__).logits

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

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

																	ppls += perplexity_batch.tolist()

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






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:Optional[Any]			  =   "▁"

lowercase__		:str			  =   {"vocab_file": "prophetnet.tokenizer"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase__		:Optional[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase__		:List[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						collections.OrderedDict()
					with open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as reader:
										lowercase						     =						reader.readlines()
					for index, token in enumerate(lowerCAmelCase__		):
										lowercase						     =						token.rstrip('''\n'''		)
										lowercase						     =						index
					return vocab







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	Any	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Any	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[UNK]"       ,A__="[PAD]"       ,A__="[CLS]"       ,A__="[MASK]"       ,A__ = None       ,**A__       ,):
												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,unk_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))
												lowercase						     =						vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												lowercase						     =						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

												for i in range(1_0):
																	lowercase						     =						f'[unused{i}]'
																	lowercase						     =						5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowercase						     =						1_2
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(A__)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]




							@property
							def 			A__				(    self):
												return len(self.sp_model) + self.fairseq_offset




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)





							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




