


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
	from ... import PreTrainedTokenizerBase, TensorType

A							:					Tuple     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""vision-encoder-decoder"""
					snake_case_		:				Union[str, Any]										=	True


					def __init__(						self	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	Dict:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)
											if "encoder" not in kwargs or "decoder" not in kwargs:
																	raise ValueError(
																	    f'''A configuraton of type {self.model_type} cannot be instantiated because '''
																	    f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''						)

											__snake_case				=		kwargs.pop("encoder"						)
											__snake_case				=		encoder_config.pop("model_type"						)
											__snake_case				=		kwargs.pop("decoder"						)
											__snake_case				=		decoder_config.pop("model_type"						)

											__snake_case				=		AutoConfig.for_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		AutoConfig.for_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		True


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    **SCREAMING_SNAKE_CASE	:		int						)			->	PretrainedConfig:




											'''simple docstring'''

											logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"						)
											__snake_case				=		True
											__snake_case				=		True

											return cls(encoder=encoder_config.to_dict()     ,    decoder=decoder_config.to_dict()     ,    **SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		copy.deepcopy(self.__dict__						)
											__snake_case				=		self.encoder.to_dict()
											__snake_case				=		self.decoder.to_dict()
											__snake_case				=		self.__class__.model_type
											return output






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	float:




											'''simple docstring'''

											return 1e-4






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}						)






class     UpperCamelCase(  _a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											__snake_case				=		OrderedDict()
											__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
											__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
											__snake_case				=		{0: "batch", 1: "encoder_sequence"}

											return common_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		"PreTrainedTokenizerBase"     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional["TensorType"] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											import torch

											__snake_case				=		OrderedDict()

											__snake_case				=		super().generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											__snake_case		,					__snake_case				=		dummy_input["input_ids"].shape
											__snake_case				=		(batch, encoder_sequence, self._config.encoder_hidden_size)
											__snake_case				=		dummy_input.pop("input_ids"						)
											__snake_case				=		dummy_input.pop("attention_mask"						)
											__snake_case				=		torch.zeros(SCREAMING_SNAKE_CASE						)

											return common_inputs






class     UpperCamelCase(  _a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	None:




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig						)			->	OnnxConfig:




											'''simple docstring'''

											return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		PretrainedConfig     ,    SCREAMING_SNAKE_CASE	:		str = "default"						)			->	OnnxConfig:




											'''simple docstring'''

											__snake_case				=		encoder_config.hidden_size
											return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)









from sklearn.metrics import mean_squared_error

import datasets


A							:					List[Any]     						=     '\\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'

A							:					Optional[int]     						=     '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


A							:					str     						=     '\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     UpperCamelCase(  datasets.Metric   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		int="uniform_average"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		mean_squared_error(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    sample_weight=SCREAMING_SNAKE_CASE     ,    multioutput=SCREAMING_SNAKE_CASE     ,    squared=SCREAMING_SNAKE_CASE						)

											return {"mse": mse}





