



"""simple docstring"""







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			UpperCamelCase     =							"""pt"""
elif is_tf_available():
			UpperCamelCase     =							"""tf"""
else:
			UpperCamelCase     =							"""jax"""






class       UpperCamelCase__			(   _SCREAMING_SNAKE_CASE       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					str							  =     PerceiverTokenizer
			A__	:					Optional[Any]							  =     False


			def   snake_case__	(      self		)      ->	int:
				super().setUp()
				A__											=					PerceiverTokenizer()
				tokenizer.save_pretrained(self.tmpdirname		)


			@cached_property
			def   snake_case__	(      self		)      ->	Any:
				return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**_lowercase		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=20						,	SCREAMING_SNAKE_CASE__=5		)      ->	Dict:
				A__											=					[]
				for i in range(len(_lowercase		)		):
					try:
						A__											=					tokenizer.decode([i]						,	clean_up_tokenization_spaces=_lowercase		)
					except UnicodeDecodeError:
						pass
					toks.append((i, tok)		)

				A__											=					list(filter(lambda SCREAMING_SNAKE_CASE__		: re.match(R"^[ a-zA-Z]+$"						,	t[1]		)						,	_lowercase		)		)
				A__											=					list(filter(lambda SCREAMING_SNAKE_CASE__		: [t[0]] == tokenizer.encode(t[1]						,	add_special_tokens=_lowercase		)						,	_lowercase		)		)
				if max_length is not None and len(_lowercase		) > max_length:
					A__											=					toks[:max_length]
				if min_length is not None and len(_lowercase		) < min_length and len(_lowercase		) > 0:
					while len(_lowercase		) < min_length:
						A__											=					toks + toks
        # toks_str = [t[1] for t in toks]
				A__											=					[t[0] for t in toks]

				# Ensure consistency
				A__											=					tokenizer.decode(_lowercase						,	clean_up_tokenization_spaces=_lowercase		)
				if " " not in output_txt and len(_lowercase		) > 1:
					A__											=					(
					    tokenizer.decode([toks_ids[0]]						,	clean_up_tokenization_spaces=_lowercase		)
					    + " "
					    + tokenizer.decode(toks_ids[1:]						,	clean_up_tokenization_spaces=_lowercase		)
					)
				if with_prefix_space:
					A__											=					" " + output_txt
				A__											=					tokenizer.encode(_lowercase						,	add_special_tokens=_lowercase		)
				return output_txt, output_ids


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.perceiver_tokenizer
				A__											=					"Unicode €."
				A__											=					tokenizer(_lowercase		)
				A__											=					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
				self.assertEqual(encoded["input_ids"]						,	_lowercase		)

				# decoding
				A__											=					tokenizer.decode(_lowercase		)
				self.assertEqual(_lowercase						,	"[CLS]Unicode €.[SEP]"		)

				A__											=					tokenizer("e è é ê ë"		)
				A__											=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
				self.assertEqual(encoded["input_ids"]						,	_lowercase		)
				# decoding
				A__											=					tokenizer.decode(_lowercase		)
				self.assertEqual(_lowercase						,	"[CLS]e è é ê ë[SEP]"		)

				# encode/decode, but with `encode` instead of `__call__`
				self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"		)		)						,	"[CLS]e è é ê ë[SEP]"		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.perceiver_tokenizer
				A__											=					["A long paragraph for summarization.", "Another paragraph for summarization."]
				# fmt: off
				A__											=					[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
				# fmt: on
				A__											=					tokenizer(_lowercase						,	padding=_lowercase						,	return_tensors=_lowercase		)
				self.assertIsInstance(_lowercase						,	_lowercase		)

				if FRAMEWORK != "jax":
					A__											=					list(batch.input_ids.numpy()[0]		)
				else:
					A__											=					list(batch.input_ids.tolist()[0]		)

				self.assertListEqual(_lowercase						,	_lowercase		)

				self.assertEqual((2, 38)						,	batch.input_ids.shape		)
				self.assertEqual((2, 38)						,	batch.attention_mask.shape		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.perceiver_tokenizer
				A__											=					["A long paragraph for summarization.", "Another paragraph for summarization."]
				A__											=					tokenizer(_lowercase						,	padding=_lowercase						,	return_tensors=_lowercase		)
				# check if input_ids are returned and no decoder_input_ids
				self.assertIn("input_ids"						,	_lowercase		)
				self.assertIn("attention_mask"						,	_lowercase		)
				self.assertNotIn("decoder_input_ids"						,	_lowercase		)
				self.assertNotIn("decoder_attention_mask"						,	_lowercase		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.perceiver_tokenizer
				A__											=					[
				    "Summary of the text.",
				    "Another summary.",
				]
				A__											=					tokenizer(
				    text_target=_lowercase						,	max_length=32						,	padding="max_length"						,	truncation=_lowercase						,	return_tensors=_lowercase		)
				self.assertEqual(32						,	targets["input_ids"].shape[1]		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						self.assertNotEqual(tokenizer.model_max_length						,	42		)

        # Now let's start the test
				A__											=					self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						# Isolate this from the other tests because we save additional tokens/etc
						A__											=					tempfile.mkdtemp()

						A__											=					" He is very happy, UNwant\u00E9d,running"
						A__											=					tokenizer.encode(_lowercase						,	add_special_tokens=_lowercase		)
						tokenizer.save_pretrained(_lowercase		)

						A__											=					tokenizer.__class__.from_pretrained(_lowercase		)
						A__											=					after_tokenizer.encode(_lowercase						,	add_special_tokens=_lowercase		)
						self.assertListEqual(_lowercase						,	_lowercase		)

						shutil.rmtree(_lowercase		)

				A__											=					self.get_tokenizers(model_max_length=42		)
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						# Isolate this from the other tests because we save additional tokens/etc
						A__											=					tempfile.mkdtemp()

						A__											=					" He is very happy, UNwant\u00E9d,running"
						tokenizer.add_tokens(["bim", "bambam"]		)
						A__											=					tokenizer.additional_special_tokens
						additional_special_tokens.append("new_additional_special_token"		)
						tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}		)
						A__											=					tokenizer.encode(_lowercase						,	add_special_tokens=_lowercase		)
						tokenizer.save_pretrained(_lowercase		)

						A__											=					tokenizer.__class__.from_pretrained(_lowercase		)
						A__											=					after_tokenizer.encode(_lowercase						,	add_special_tokens=_lowercase		)
						self.assertListEqual(_lowercase						,	_lowercase		)
						self.assertIn("new_additional_special_token"						,	after_tokenizer.additional_special_tokens		)
						self.assertEqual(after_tokenizer.model_max_length						,	42		)

						A__											=					tokenizer.__class__.from_pretrained(_lowercase						,	model_max_length=43		)
						self.assertEqual(tokenizer.model_max_length						,	43		)

						shutil.rmtree(_lowercase		)


			def   snake_case__	(      self		)      ->	str:
				A__											=					[]
				if self.test_slow_tokenizer:
					tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

				if self.test_rust_tokenizer:
					tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

				for tokenizer_class, tokenizer_utils in tokenizer_list:
					with tempfile.TemporaryDirectory() as tmp_dir:
						tokenizer_utils.save_pretrained(_lowercase		)

						with open(os.path.join(_lowercase						,	"special_tokens_map.json"		)						,	encoding="utf-8"		) as json_file:
							A__											=					json.load(_lowercase		)

						with open(os.path.join(_lowercase						,	"tokenizer_config.json"		)						,	encoding="utf-8"		) as json_file:
							A__											=					json.load(_lowercase		)

						A__											=					[f"""<extra_id_{i}>""" for i in range(125		)]

						A__											=					added_tokens_extra_ids + [
						    "an_additional_special_token"
						]
						A__											=					added_tokens_extra_ids + [
						    "an_additional_special_token"
						]

						with open(os.path.join(_lowercase						,	"special_tokens_map.json"		)						,	"w"						,	encoding="utf-8"		) as outfile:
							json.dump(_lowercase						,	_lowercase		)
						with open(os.path.join(_lowercase						,	"tokenizer_config.json"		)						,	"w"						,	encoding="utf-8"		) as outfile:
							json.dump(_lowercase						,	_lowercase		)

						# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
						# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
						# "special_tokens_map.json" files
						A__											=					tokenizer_class.from_pretrained(
						    _lowercase						,	)
						self.assertIn(
						    "an_additional_special_token"						,	tokenizer_without_change_in_init.additional_special_tokens		)
						self.assertEqual(
						    ["an_additional_special_token"]						,	tokenizer_without_change_in_init.convert_ids_to_tokens(
						        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]		)		)						,	)

						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
						A__											=					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"						,	lstrip=_lowercase		)]
						A__											=					tokenizer_class.from_pretrained(
						    _lowercase						,	additional_special_tokens=_lowercase						,	)

						self.assertIn("a_new_additional_special_token"						,	tokenizer.additional_special_tokens		)
						self.assertEqual(
						    ["a_new_additional_special_token"]						,	tokenizer.convert_ids_to_tokens(
						        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]		)		)						,	)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.perceiver_tokenizer
				self.assertEqual(tokenizer.decode([178]		)						,	"�"		)


			def   snake_case__	(      self		)      ->	List[str]:
				pass


			def   snake_case__	(      self		)      ->	List[Any]:
				pass


			def   snake_case__	(      self		)      ->	Optional[int]:
				pass


			def   snake_case__	(      self		)      ->	List[str]:
				pass







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_tokenizers(fast=_lowercase						,	do_lower_case=_lowercase		)
				for tokenizer in tokenizers:
					with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
						A__											=					["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
						A__											=					tokenizer.convert_tokens_to_string(_lowercase		)

						self.assertIsInstance(_lowercase						,	_lowercase		)






'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


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

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""




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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												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    _lowercase      (   self ):

												"""simple docstring"""



												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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
