



'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__          =  get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[Any]     =       XGLMTokenizer
       lowerCAmelCase__	:Tuple     =       XGLMTokenizerFast
       lowerCAmelCase__	:Any     =       True
       lowerCAmelCase__	:List[Any]     =       True



       def 				_a   (					self   )	->				Tuple:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )
           tokenizer.save_pretrained(self.tmpdirname   )



       def 				_a   (					self   )	->				Tuple:
           lowercase__									=    "<pad>"
           lowercase__									=    1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_   )  ,UpperCAmelCase_   )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_   )  ,UpperCAmelCase_   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    list(self.get_tokenizer().get_vocab().keys()   )

           self.assertEqual(vocab_keys[0]  ,"<s>"   )
           self.assertEqual(vocab_keys[1]  ,"<pad>"   )
           self.assertEqual(len(UpperCAmelCase_   )  ,1_008   )



       def 				_a   (					self   )	->				Union[str, Any]:
           self.assertEqual(self.get_tokenizer().vocab_size  ,1_008   )



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    XGLMTokenizer(UpperCAmelCase_  ,keep_accents=UpperCAmelCase_   )

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

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )  ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,)

           lowercase__									=    tokenizer.tokenize("I was born in 92000, and this is falsé."   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "9",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "é",
                   ".",
               ]  ,)
           lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]  ,)

           lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
           self.assertListEqual(
               UpperCAmelCase_  ,[
                   SPIECE_UNDERLINE + "I",
                   SPIECE_UNDERLINE + "was",
                   SPIECE_UNDERLINE + "b",
                   "or",
                   "n",
                   SPIECE_UNDERLINE + "in",
                   SPIECE_UNDERLINE + "",
                   "<unk>",
                   "2",
                   "0",
                   "0",
                   "0",
                   ",",
                   SPIECE_UNDERLINE + "and",
                   SPIECE_UNDERLINE + "this",
                   SPIECE_UNDERLINE + "is",
                   SPIECE_UNDERLINE + "f",
                   "al",
                   "s",
                   "<unk>",
                   ".",
               ]  ,)



       @cached_property
       def 				_a   (					self   )	->				Optional[int]:
           return XGLMTokenizer.from_pretrained("facebook/xglm-564M"   )



       def 				_a   (					self   )	->				int:
           with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(UpperCAmelCase_  ,f.name   )
               lowercase__									=    XGLMTokenizer(f.name  ,keep_accents=UpperCAmelCase_   )
               lowercase__									=    pickle.dumps(UpperCAmelCase_   )
           pickle.loads(UpperCAmelCase_   )



       def 				_a   (					self   )	->				Optional[int]:
           if not self.test_rust_tokenizer:
               return

           lowercase__									=    self.get_tokenizer()
           lowercase__									=    self.get_rust_tokenizer()

           lowercase__									=    "I was born in 92000, and this is falsé."

           lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.tokenize(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    self.get_rust_tokenizer()
           lowercase__									=    tokenizer.encode(UpperCAmelCase_   )
           lowercase__									=    rust_tokenizer.encode(UpperCAmelCase_   )
           self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )



       @slow
       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    "Hello World!"
           lowercase__									=    [2, 31_227, 4_447, 35]

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )



       @slow
       def 				_a   (					self   )	->				str:
           lowercase__									=    (
               "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
               " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
           )
           # fmt: off
           lowercase__									=    [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
           # fmt: on

           self.assertListEqual(UpperCAmelCase_  ,self.big_tokenizer.encode(UpperCAmelCase_   )   )







       @slow
       def 				_a   (					self   )	->				str:
           # fmt: off
           lowercase__									=    {
               "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
               "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]]
           }  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase_  ,model_name="facebook/xglm-564M"  ,padding=UpperCAmelCase_  ,)









'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION )
class 					snake_case			(datasets.Metric ):



       def 				_a   (					self   )	->				Optional[int]:
           return datasets.MetricInfo(
               description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types()   )  ,reference_urls=[
                   "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"   )   ),
                   "references": datasets.Sequence(datasets.Value("float"   )   ),
               }
           else:
               return {
                   "predictions": datasets.Value("float"   ),
                   "references": datasets.Value("float"   ),
               }







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}




