

from sklearn.metrics import mean_squared_error

import datasets


_a:    Any 							=			"""\
@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}
}
"""

_a:    List[Any] 							=			"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_a:    List[str] 							=			"""
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						):




	def       __A					(		self	:						List[str]							):


		'''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       __A					(		self	:						Optional[Any]							):


		'''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       __A					(		self	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						str="uniform_average"		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			mean_squared_error(
		    lowerCAmelCase		,	lowerCAmelCase		,	sample_weight=lowerCAmelCase		,	multioutput=lowerCAmelCase		,	squared=lowerCAmelCase							)

		return {"mse": mse}


import warnings
from typing import Dict, List, Optional, Tuple

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


_a:    Dict 							=			logging.get_logger(__name__)

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]="</s>"		,	lowerCAmelCase	:						str="<unk>"		,	lowerCAmelCase	:						Optional[int]="<pad>"		,	lowerCAmelCase	:						List[str]=125		,	lowerCAmelCase	:						List[Any]=None		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







		if extra_ids > 0 and additional_special_tokens is None:
			UpperCAmelCase_       				=			[F"<extra_id_{i}>" for i in range(lowerCAmelCase							)]
		elif extra_ids > 0 and additional_special_tokens is not None:
			# Check that we have the right number of extra_id special tokens
			UpperCAmelCase_       				=			len(set(filter(lambda lowerCAmelCase							: bool("extra_id" in str(lowerCAmelCase							)							)		,	lowerCAmelCase							)							)							)
			if extra_tokens != extra_ids:
				raise ValueError(
				    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
				    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
				    " extra_ids tokens"							)

		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else pad_token
		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else eos_token
		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else unk_token

		super().__init__(
		    eos_token=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	extra_ids=lowerCAmelCase		,	additional_special_tokens=lowerCAmelCase		,	**lowerCAmelCase		,	)

		UpperCAmelCase_       				=			extra_ids

		UpperCAmelCase_       				=			2**8  # utf is 8 bits

		# define special tokens dict
		UpperCAmelCase_       				=			{
		    self.pad_token: 0,
		    self.eos_token: 1,
		    self.unk_token: 2,
		}
		UpperCAmelCase_       				=			len(self.special_tokens_encoder							)
		UpperCAmelCase_       				=			len(lowerCAmelCase							)
		for i, token in enumerate(lowerCAmelCase							):
			UpperCAmelCase_       				=			self.vocab_size + i - n
		UpperCAmelCase_       				=			{v: k for k, v in self.special_tokens_encoder.items()}




	@property
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = 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							)

		# normal case: some special tokens
		if token_ids_a is None:
			return ([0] * len(lowerCAmelCase							)) + [1]
		return ([0] * len(lowerCAmelCase							)) + [1] + ([0] * len(lowerCAmelCase							)) + [1]




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[int]							):


		'''simple docstring'''







		if len(lowerCAmelCase							) > 0 and token_ids[-1] == self.eos_token_id:
			warnings.warn(
			    F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
			    " eos tokens being added."							)
			return token_ids
		else:
			return token_ids + [self.eos_token_id]




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[self.eos_token_id]

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




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._add_eos_if_not_present(lowerCAmelCase							)
		if token_ids_a is None:
			return token_ids_a
		else:
			UpperCAmelCase_       				=			self._add_eos_if_not_present(lowerCAmelCase							)
			return token_ids_a + token_ids_a




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[chr(lowerCAmelCase							) for i in text.encode("utf-8"							)]
		return tokens




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		if token in self.special_tokens_encoder:
			UpperCAmelCase_       				=			self.special_tokens_encoder[token]
		elif token in self.added_tokens_encoder:
			UpperCAmelCase_       				=			self.added_tokens_encoder[token]
		elif len(lowerCAmelCase							) != 1:
			UpperCAmelCase_       				=			self.unk_token_id
		else:
			UpperCAmelCase_       				=			ord(lowerCAmelCase							) + self._num_special_tokens
		return token_id




	def       __A					(		self	:						str		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		if index in self.special_tokens_decoder:
			UpperCAmelCase_       				=			self.special_tokens_decoder[index]
		else:
			UpperCAmelCase_       				=			chr(index - self._num_special_tokens							)
		return token




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			b""
		for token in tokens:
			if token in self.special_tokens_decoder:
				UpperCAmelCase_       				=			self.special_tokens_decoder[token].encode("utf-8"							)
			elif token in self.added_tokens_decoder:
				UpperCAmelCase_       				=			self.special_tokens_decoder[token].encode("utf-8"							)
			elif token in self.special_tokens_encoder:
				UpperCAmelCase_       				=			token.encode("utf-8"							)
			elif token in self.added_tokens_encoder:
				UpperCAmelCase_       				=			token.encode("utf-8"							)
			else:
				UpperCAmelCase_       				=			bytes([ord(lowerCAmelCase							)]							)
			bstring += tok_string
		UpperCAmelCase_       				=			bstring.decode("utf-8"		,	errors="ignore"							)
		return string




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		return ()