


"""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__  							="\\n\n"

UpperCAmelCase__  							="\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__  							="\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 SCREAMING_SNAKE_CASE_	(		self	:	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	:	Union[str, Any]      ,       A_	:	Union[str, Any]      ,       A_	:	Tuple      ,       A_	:	int = 1_6      ,       A_	:	bool = True      ,       A_	:	Optional[Any]=None							):






							'''simple docstring'''


							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(lowerCAmelCase__							)
							__lowercase									=	model.to(lowerCAmelCase__							)

							__lowercase									=	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:
								__lowercase									=	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"
								__lowercase									=	model.config.max_length - 1
							else:
								__lowercase									=	model.config.max_length

							__lowercase									=	tokenizer(
							    lowerCAmelCase__      ,       add_special_tokens=lowerCAmelCase__      ,       padding=lowerCAmelCase__      ,       truncation=lowerCAmelCase__      ,       max_length=lowerCAmelCase__      ,       return_tensors="""pt"""      ,       return_attention_mask=lowerCAmelCase__      ,       ).to(lowerCAmelCase__							)

							__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(lowerCAmelCase__							)      ,       lowerCAmelCase__							)							):
								__lowercase									=	min(start_index + batch_size      ,       len(lowerCAmelCase__							)							)
								__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(lowerCAmelCase__							)
									__lowercase									=	torch.cat([bos_tokens_tensor, encoded_batch]      ,       dim=1							)
									__lowercase									=	torch.cat(
									    [torch.ones(bos_tokens_tensor.size()      ,       dtype=torch.intaa							).to(lowerCAmelCase__							), attn_mask]      ,       dim=1							)

								__lowercase									=	encoded_batch

								with torch.no_grad():
									__lowercase									=	model(lowerCAmelCase__      ,       attention_mask=lowerCAmelCase__							).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							)      ,       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__							)}




"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       UpperCAmelCase__  							=get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__  							=12_8022
UpperCAmelCase__  							=12_8028





@require_sentencepiece
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Dict											=		MaMaaaTokenizer
      a :     Optional[int]											=		False
      a :     str											=		False
      a :     Any											=		True
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       super().setUp()

       __lowercase									=	["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase									=	dict(zip(A_      ,       range(len(A_							)							)							)							)
       __lowercase									=	Path(self.tmpdirname							)
       save_json(A_      ,       save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
        copyfile(A_      ,       save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

       __lowercase									=	MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       **A_	:	List[Any]							):






       '''simple docstring'''


       return MaMaaaTokenizer.from_pretrained(self.tmpdirname      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	"""</s>"""
       __lowercase									=	0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_							)      ,       A_							)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_							)      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	self.get_tokenizer()
       __lowercase									=	list(tokenizer.get_vocab().keys()							)

       self.assertEqual(vocab_keys[0]      ,       """</s>"""							)
       self.assertEqual(vocab_keys[1]      ,       """<unk>"""							)
       self.assertEqual(vocab_keys[-1]      ,       """<s>"""							)
       self.assertEqual(len(A_							)      ,       tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)
      @unittest.skip("""Skip this test while all models are still to be uploaded."""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.get_tokenizer()

       __lowercase									=	tokenizer.tokenize("""This is a test"""							)
       self.assertListEqual(A_      ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_							)      ,       [2, 3, 4, 5, 6]      ,       )

       __lowercase									=	tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
       self.assertListEqual(A_      ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

       __lowercase									=	tokenizer.convert_tokens_to_string(A_							)
       self.assertEqual(A_      ,       """This is a test"""							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	{"""input_ids""": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_      ,       model_name="""facebook/m2m100_418M"""      ,       revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""      ,       )






@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__						(		unittest.TestCase    ):
      a :     Optional[int]											=		"""facebook/m2m100_418M"""
      a :     str											=		[
          """In my opinion, there are two levels of response from the French government.""",
          """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
      ]
      a :     int											=		[
          """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
          """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
      ]

      # fmt: off
      a :     Optional[int]											=		[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	str							):






       '''simple docstring'''


       __lowercase									=	MaMaaaTokenizer.from_pretrained(
           cls.checkpoint_name      ,       src_lang="""en"""      ,       tgt_lang="""fr"""							)
       __lowercase									=	1
       return cls
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)      ,       1_2_8_0_0_6							)
       self.assertEqual(self.tokenizer.get_lang_id("""en"""							)      ,       1_2_8_0_2_2							)
       self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)      ,       1_2_8_0_7_6							)
       self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)      ,       1_2_8_0_6_3							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer.get_vocab()
       self.assertEqual(len(A_							)      ,       self.tokenizer.vocab_size							)
       self.assertEqual(vocab["""<unk>"""]      ,       3							)
       self.assertIn(self.tokenizer.get_lang_token("""en"""							)      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	"""en"""
       __lowercase									=	self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
       self.assertListEqual(self.expected_src_tokens      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       self.assertIn(A_      ,       self.tokenizer.all_special_ids							)
       # fmt: off
       __lowercase									=	[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
       # fmt: on
       __lowercase									=	self.tokenizer.decode(A_      ,       skip_special_tokens=A_							)
       __lowercase									=	self.tokenizer.decode(generated_ids[1:]      ,       skip_special_tokens=A_							)
       self.assertEqual(A_      ,       A_							)
       self.assertNotIn(self.tokenizer.eos_token      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	tempfile.mkdtemp()
       __lowercase									=	self.tokenizer.lang_token_to_id
       self.tokenizer.save_pretrained(A_							)
       __lowercase									=	MaMaaaTokenizer.from_pretrained(A_							)
       self.assertDictEqual(new_tok.lang_token_to_id      ,       A_							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	"""en"""
       __lowercase									=	"""fr"""

       __lowercase									=	self.tokenizer(self.src_text      ,       text_target=self.tgt_text      ,       padding=A_      ,       return_tensors="""pt"""							)

       __lowercase									=	shift_tokens_right(
           batch["""labels"""]      ,       self.tokenizer.pad_token_id      ,       self.tokenizer.eos_token_id							)

       for k in batch:
        __lowercase									=	batch[k].tolist()
       # batch = {k: v.tolist() for k,v in batch.items()}
       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       # batch.decoder_inputs_ids[0][0] ==
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == FR_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	"""mr"""
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""mr"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)

       __lowercase									=	"""zh"""
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""zh"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	"""mr"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""mr"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

       __lowercase									=	"""zh"""
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id("""zh"""							)]							)
       self.assertListEqual(self.tokenizer.suffix_tokens      ,       [self.tokenizer.eos_token_id]							)
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens      ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)






      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer._build_translation_inputs("""A test"""      ,       return_tensors="""pt"""      ,       src_lang="""en"""      ,       tgt_lang="""ar"""							)

       self.assertEqual(
           nested_simplify(A_							)      ,       {
               # en_XX, A, test, EOS
               """input_ids""": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 1_2_8_0_0_6,
           }      ,       )
