
"""simple docstring"""

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

import datasets
from datasets import logging


UpperCamelCase	:				Any  	= "\\n\n"

UpperCamelCase	:				Union[str, 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"

UpperCamelCase	:				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      ):

      '''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 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase = 16	,     __UpperCAmelCase = True	,     __UpperCAmelCase=None      ):

      '''simple docstring'''




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

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

      __UpperCamelCase				  =       AutoTokenizer.from_pretrained(__lowerCAmelCase      )

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

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

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

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

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

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

       __UpperCamelCase				  =       encoded_batch

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

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

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

       ppls += perplexity_batch.tolist()

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










import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      "▁"

_a 						=      {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_a 						=      {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_a 						=      {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
_a 						=      {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						VOCAB_FILES_NAMES
			lowerCAmelCase_							     =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_							     =						PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_							     =						["""input_ids""", """attention_mask"""]

			lowerCAmelCase_							     =						[]
			lowerCAmelCase_							     =						[]





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase="<s>"       ,  __lowerCAmelCase="</s>"       ,  __lowerCAmelCase="</s>"       ,  __lowerCAmelCase="<pad>"       ,  __lowerCAmelCase="<unk>"       ,  __lowerCAmelCase="m2m100"       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase=8       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCamelCase__								=		language_codes
				lowerCamelCase__								=		FAIRSEQ_LANGUAGE_CODES[language_codes]
				lowerCamelCase__								=		{lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

				lowerCamelCase__								=		kwargs.get('''additional_special_tokens'''       ,  []							)
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(__lowerCAmelCase							)
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(__lowerCAmelCase							) not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__lowerCAmelCase       ,  tgt_lang=__lowerCAmelCase       ,  bos_token=__lowerCAmelCase       ,  eos_token=__lowerCAmelCase       ,  sep_token=__lowerCAmelCase       ,  unk_token=__lowerCAmelCase       ,  pad_token=__lowerCAmelCase       ,  language_codes=__lowerCAmelCase       ,  sp_model_kwargs=self.sp_model_kwargs       ,  num_madeup_words=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )

				lowerCamelCase__								=		vocab_file
				lowerCamelCase__								=		load_json(__lowerCAmelCase							)
				lowerCamelCase__								=		{v: k for k, v in self.encoder.items()}
				lowerCamelCase__								=		spm_file
				lowerCamelCase__								=		load_spm(__lowerCAmelCase       ,  self.sp_model_kwargs							)

				lowerCamelCase__								=		len(self.encoder							)

				lowerCamelCase__								=		{
				    self.get_lang_token(__lowerCAmelCase							): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase							)
				}
				lowerCamelCase__								=		{lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase							)}
				lowerCamelCase__								=		{v: k for k, v in self.lang_token_to_id.items()}

				lowerCamelCase__								=		src_lang if src_lang is not None else '''en'''
				lowerCamelCase__								=		tgt_lang
				lowerCamelCase__								=		self.get_lang_id(self._src_lang							)
				self.set_src_lang_special_tokens(self._src_lang							)

				lowerCamelCase__								=		num_madeup_words





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return len(self.encoder							) + len(self.lang_token_to_id							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._src_lang





			@src_lang.setter
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		new_src_lang
				self.set_src_lang_special_tokens(self._src_lang							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.encode(__lowerCAmelCase       ,  out_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				if token in self.lang_token_to_id:
					return self.lang_token_to_id[token]
				return self.encoder.get(__lowerCAmelCase       ,  self.encoder[self.unk_token]							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				if index in self.id_to_lang_token:
					return self.id_to_lang_token[index]
				return self.decoder.get(__lowerCAmelCase       ,  self.unk_token							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				lowerCamelCase__								=		''''''
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						out_string += self.sp_model.decode(__lowerCAmelCase							) + token
						lowerCamelCase__								=		[]
					else:
						current_sub_tokens.append(__lowerCAmelCase							)
				out_string += self.sp_model.decode(__lowerCAmelCase							)
				return out_string.strip()





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = False							):



				'''simple docstring'''







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

				lowerCamelCase__								=		[1] * len(self.prefix_tokens							)
				lowerCamelCase__								=		[1] * len(self.suffix_tokens							)
				if token_ids_a is None:
					return prefix_ones + ([0] * len(__lowerCAmelCase							)) + suffix_ones
				return prefix_ones + ([0] * len(__lowerCAmelCase							)) + ([0] * len(__lowerCAmelCase							)) + suffix_ones





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				if token_ids_a is None:
					return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{self.convert_ids_to_tokens(__lowerCAmelCase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab





			def __getstate__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.__dict__.copy()
				lowerCamelCase__								=		None
				return state





			def __setstate__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		d

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

				lowerCamelCase__								=		load_spm(self.spm_file       ,  self.sp_model_kwargs							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		Path(__lowerCAmelCase							)
				if not save_dir.is_dir():
					raise OSError(F'{save_directory} should be a directory'							)
				lowerCamelCase__								=		save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
				)
				lowerCamelCase__								=		save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
				)

				save_json(self.encoder       ,  __lowerCAmelCase							)

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

				return (str(__lowerCAmelCase							), str(__lowerCAmelCase							))





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = "en"       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = "ro"       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		src_lang
				lowerCamelCase__								=		tgt_lang
				self.set_src_lang_special_tokens(self.src_lang							)
				return super().prepare_seqaseq_batch(__lowerCAmelCase       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if src_lang is None or tgt_lang is None:
					raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
				lowerCamelCase__								=		src_lang
				lowerCamelCase__								=		self(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_lang_id(__lowerCAmelCase							)
				lowerCamelCase__								=		tgt_lang_id
				return inputs





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.set_src_lang_special_tokens(self.src_lang							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.set_tgt_lang_special_tokens(self.tgt_lang							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				lowerCamelCase__								=		self.lang_token_to_id[lang_token]
				lowerCamelCase__								=		[self.cur_lang_id]
				lowerCamelCase__								=		[self.eos_token_id]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				lowerCamelCase__								=		self.lang_token_to_id[lang_token]
				lowerCamelCase__								=		[self.cur_lang_id]
				lowerCamelCase__								=		[self.eos_token_id]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.lang_code_to_token[lang]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				return self.lang_token_to_id[lang_token]





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      sentencepiece.SentencePieceProcessor:







	'''simple docstring'''







	lowerCamelCase__								=		sentencepiece.SentencePieceProcessor(**__snake_case							)
	spm.Load(str(__snake_case							)							)
	return spm





def lowerCAmelCase__(__snake_case							)				->      Union[Dict, List]:







	'''simple docstring'''







	with open(__snake_case       ,'''r'''							) as f:
		return json.load(__snake_case							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      None:







	'''simple docstring'''







	with open(__snake_case       ,'''w'''							) as f:
		json.dump(__snake_case       ,__snake_case       ,indent=2							)



